ISBA Newsletter - March 1999

It is fitting to begin this issue of the ISBA Newsletter with thanks to the previous editor, Mike Evans, for a job very well done. In particular, Mike served on an ad hoc basis, taking on tasks above and beyond the call of his position as ISBA secretary. All hail! It is just as fitting to thank the new editor, Fabrizio Ruggeri, for the energy and leadership that are evident everywhere in this new and reorganized Newsletter. This issue is just great, and gives promise of great things to come.

Since this is my first "word" as President, I reviewed the ISBA mission statement, which is at the ISBA home page. It begins, "ISBA was founded in 1992 to promote the development and application of Bayesian statistical theory and methods useful in the solution of theoretical and applied problems in science, industry and government. By sponsoring and organizing meetings and other activities the ISBA provides a focal point for those interested in Bayesian inference and its applications." This Newsletter provides lots of information about the meetings and activities of ISBA - in particular, the quadrennial world meeting of ISBA in Crete in May, 2000.

The ISBA mission statement focuses on developing solutions to real problems. I have always found this focus to be very fruitful in my primary academic field of economics. An audience of economists, even an audience of econometricians, will often go to sleep if the main topic is the weak sufficiency principle, Lindley's paradox, or the latest nuance in Markov chain Monte Carlo methodology. (If one is lucky. One can produce outright hostility given a little effort.) That same audience, if presented with the solution of a real, applied, problem, will sit up and take notice. The more important and common is the problem, the greater will be the interest. In my experience the fact that the solution involves Bayesian methodology is of at most secondary interest, the first time an economist encounters a Bayesian approach. But as the experience is repeated, econometricians and other methodologically inclined economists naturally seek out the common denominator, and for some this leads to serious study of the weak sufficiency principle, Lindley's paradox, and so on. Many will never take this step: they are practical people looking for solutions that work, and are happy to apply them without plumbing the depths of methodology. I have always thought that this reaction by practical people is the ultimate recognition of the utility of Bayesian methods.

As you look at this issue of the Newsletter you will see, alongside the stories of meetings and events, indications of this practical success of Bayesian methods. I call your attention in particular to the article on the Ph.D. thesis by Patrick Bajari, "The First Price Auction with Asymmetric Bidders: Theory and Applications," which won the Zellner Award in 1997. The Zellner award is given by the editorial board of the Journal of Business and Economic Statistics to the best thesis in economics and statistics each year. The award is not for the best Bayesian thesis, and the committee is not comprised of Bayesians. Bajari's thesis makes advances in economic theory and applies them to the practical problem of designing bidding systems for road repair contracts so as to reduce expenditures by local governments. Theory and data meet through Bayesian inference using applications of recently developed MCMC methods. The focus of the thesis is not Bayesian, but the problem could not be solved with non-Bayesian methods. Many economists have take notice! Through the repetition of this sort of success, Bayesian methods will spread in science.

Starting with this issue, you will see a completely new ISBA Newsletter (NL), with a new Editor and, for the first time in its history, an Editorial Board. The NL is the result of the efforts of many people: Associate and Corresponding Editors (see their names in the last page), those who contributed a column to the issue, many of you who discussed a proposal for the ``new'' NL and gave valuable suggestions, the ISBA officers, my colleagues at IAMI, the staff at ISDS. I wish to thank all of them.

Now it is your turn. We are looking forward to receiving your comments and contributions to the NL. A section on Letters will be started as soon as we get any. Moreover, the Associate Editors will be happy to consider your suggestions for topics to be covered in future issues. I am looking forward to getting your feedback on the NL, suggestions and criticisms, proposals for new sections (e.g. Book reviews).

We would like the NL to become a valuable source of information and a place for discussion. Nowadays, news on conferences, prizes, etc. is spread very quickly through Internet. It makes little sense to have a NL focusing just on this type of information though we do plan to have a news section (News from the world), in which a short presentation of the facts will be followed by an e-mail or a web address. At the same time, we do not plan to become a scientific journal: this is not the purpose of a NL. We will though have a section on Bayesian History, where little known facts will be brought to the knowledge of a larger audience, stimulating, hopefully, further reading on those topics. In this issue Prof. Regazzini presents a little known paper on Bayesian nonparametrics by de Finetti.

The past, present and future of Bayesian statistics will be discussed by leading Bayesians in the section on Interviews: we start with Prof. Zellner; a quite natural choice as one of the founding members of ISBA!

Dissemination of Bayesian ideas is, of course, important. This starts from the courses that many of us are teaching. The section on Teaching will be a place where teaching experiences and tools are discussed. Dissemination is possible also if Bayesian methods are successfully applied to real problems: a section on Applications will describe some key Bayesian contributions.

The sections on Software and Bibliography should help researchers by pointing out new software and providing references on promising, but less well known fields.

We also provide a section for Bayesians of the future. A Ph.D. student is in charge of the Students' corner, where students can discuss issues of common interest. In this issue we present abstracts of Ph.D. dissertations discussed in 1998 at ISDS, Duke University. We encourage Departments to send us abstracts of their Ph.D. dissertations.
We would like to spread information about Bayesian activities in countries where the use of Bayesian ideas is a quite recent phenomenon: we start with Venezuela and we learn how the local Bayesian group started and grew.

We have not forgotten that our NL is the ``official newsletter'' of ISBA; thus readers will find news on the ISBA's activity too. In this issue, we start with the call for session organisers and papers for ISBA 2000, the 6^th world meeting of ISBA.

A final word about the NL and its Editorial board: the Associate Editors (AE) have been appointed for a two-years term (except the AE of the Students' corner who has to write his dissertation!), whereas the Corresponding Editors's term lasts one year to get a larger number of ISBA members involved in the NL. The NL will be published 4 times a year. In early messages sent to bayes-news and ISBA members, we asked people to tell us if they wanted the NL by e-mail. Because of the small number of answers, we have decided, for the moment, to mail the NL to all the ISBA members. In a future we might think again of an electronic delivery.

This issue is sent to all the Bayesians (and non Bayesians) who have requested it, regardless of their membership in ISBA. We hope that most of them will take the little step to fill the form at the end of the NL, turn it in to Prof. Valen Johnson and become part of ISBA (and get the next issues of the NL, as well ...)

Call for session organisers and papers

ISBA 2000

The 6th World Meeting of the International Society for Bayesian Analysis

Hersonissos, Crete

May 28-June 1 2000

The Scientific Committee solicits proposals from individuals interested in organising sessions, and also from authors of individual papers. A major focus of the meeting is Bayesian methods in government, official statistics and public policy, and several sessions will be reserved for presentations on topics related to the theme. Beyond this, the meeting will feature sessions on research and applications of Bayesian statistics in many diverse areas, especially on topics consistent with the interdisciplinary outlook and mission of ISBA. Talks will be organised in sessions of 2 or (mainly) 3 presentations, each talk being 30 minutes long, including discussion time. We invite proposals from
$\bullet$ session organisers interested in putting together a session of 2 or 3 talks on a specific topic
$\bullet$ contributing speakers on any topic.

We particularly encourage proposals in areas related to the meeting theme. Suggested session topics are listed below, though proposals need not be restricted to these areas. Possible Theme topics:

$\bullet$ Bayesian methods and official statistics
$\bullet$ Statistics in the European Union
$\bullet$ Bayesian statistics in national and international finance
$\bullet$ Confidentiality and disclosure limitation
$\bullet$ Economic and governmental forecasting
$\bullet$ Government accounting and/or auditing
$\bullet$ Risk assessment (environmental, economic/business, etc)
$\bullet$ Bayesian methods in insurance
$\bullet$ Bayesian methods in agriculture
$\bullet$ Decision analysis in government policy
$\bullet$ Statistics in education and education policy
$\bullet$ Bayesian methods in political science
$\bullet$ Bayesian methods in sampling and census, including non-response in surveys
$\bullet$ Bayesian statistics in public health (disease mapping, health care policy, program evaluation, etc).
Under the general theme of government and official statistics, we solicit proposals for one or more formatted sessions with two or three speakers presenting a coordinated discussion of important statistical and societal problems. Such a session might involve a subject matter expert to present the problem and background work, followed by one or two speakers discussing specific Bayesian approaches and contributions, highlighting challenges and unsolved problems, and possibly discussing advantages of Bayesian methods over earlier (Bayesian and non-Bayesian) approaches. In addition to the theme topics, we encourage proposals for sessions and talks in any area of Bayesian statistics and decision science, including topics in theory, methodology and applications.

Proposals from session organisers must include the names of proposed speakers (preferably 3 speakers) and a session chair. Proposers of such sessions will assume responsibility for managing the speakers and ensuring a successful session at the meeting. Tentative titles of all talks should be included with the names of proposed speakers. Individual authors should simply send a proposed title for their talk. All proposals should include full e-mail addresses of all named participants, as well as their institutional affiliations. All individuals interested in presenting at the meeting will be able to do so, in either a plenary or parallel session of talks, or in evening poster sessions. Individuals who prefer to present in a poster session should mention that on their submission. All proposals should be e-mailed to Mike West, and all future correspondence with the Scientific Committee will be carried out electronically. Enquiries can be e-mailed to any member of the Scientific Committee. To be given full consideration, proposals for sessions or for individual talks must be received no later than October 1, 1999. While we will accept proposals that are received later than the October 1 deadline, we cannot promise that all organizers' requests will be met. This call for participation, and other details of ISBA 2000, is available at the ISBA web site. under the Meetings link.

Professor Zellner is a founding member of ISBA and was president of ISBA in 1994-1995. ISBA gave him a Founder's Award Plaque in 1998. Professor Zellner was President of the American Statistical Association (ASA) in 1991, first Chair of the ASA Section on Bayesian Statistical Science in 1993 and Seminar Leader of the NBER-NSF Bayesian Seminar for 25 years. He has worked at the University of Chicago since 1966 and is Distinguished Service Professor Emeritus of Economics and Statistics. He has worked in Bayesian statistics and economics for more than 30 years and has published more than 200 articles, monographs and books, many of which have been extremely influential in Bayesian research. He has received numerous awards and worked with statisticians and economists all over the world. A fuller description of his work can be seen at his homepage.

We e-mailed Professor Zellner a number of questions about his career and the Bayesian world in general. Here are his responses.

1. Why did you decide to become a statistician?

After getting an undergraduate degree in physics at Harvard, I completed about a year and a half of graduate work in physics at U. of California at Berkeley where I interacted with my brother Norman and his friends who were doing doctoral work in quantitative economics. I became aware of a great opportunity to develop and use quantitative methods and data to solve economic and business problems. I have been pursuing these objectives for many years and have come to view methods for learning from data and making decisions, that is statistics, as the foundation of all the sciences.

Since reading Sir Harold Jeffreys's books in the 1960s, I was impressed by the central role that his axioms and successful applied studies gave to Bayes's Theorem and uses of it in estimation, testing, prediction, etc. Thus, I started a program of research to compare Bayesian and non-Bayesian statistical solutions to various problems. Over the years, I and others found that Bayesian solutions were generally better and thus I and many others were happy to become identified as BAYESIAN.

2. Can you name some of the people and events that have had a great influence on you during your career?

In addition to interaction with my PhD. thesis advisors George Kuznets, Ivan Lee and Robert Gordon, who were very helpful, interaction with George Box in the 1960s when I was a faculty member at the U.of Wisconsin was very stimulating given his deep understanding of statistical theory and application. Then too, Sir Harold Jeffreys's work and comments were and are extremely influential with respect to my theoretical and applied Bayesian work. Jeffreys's philosophy, simplicity postulate, invariant priors, information theory measures, and applications had an extremely important influence on me and my work. On the several occasions when I visited with Jeffreys at Cambridge, our conversations were most constructive. The same can be said with respect to many interactions with George Barnard and Jack Good. Both offered constructive comments and helpful references on many occasions. In addition, it was Barnard and Jenkins' JRSS paper in the 1960s on a "weighted likelihood" approach to the analysis of time series models that led me to view the weighting function as a prior density and to realize that this then produced exact finite sample inferences for time series models...no asymptotics needed!!!! What a lovely realization that was. Then too, George Barnard and Edwin Jaynes have been very constructive in the late 1980s and 1990s in connection with my derivation of optimal information processing rules, including Bayes's Theorem, and my development of the Bayesian method of moments (BMOM) that yields inverse probability statements regarding parameters and future observations without the use of a likelihood function and Bayes's Theorem. We have now tested, using post data odds, BMOM and traditional Bayesian predictive densities using data,
a procedure that appealed very much to George Barnard. Last I have been very fortunate to have had many able colleagues and graduate students work with me on my NSF grants from the 1960s to the present, many of whom co-authored papers with me (see papers listed on my homepage.) Our hard work and long discussions were very influential in shaping my thoughts as were the many comments received at semi-annual meetings of the NBER-NSF Seminar on Bayesian Inference from 1970 and thereafter. While at the U. of Chicago since 1966, the following past and present colleagues have been very influential and helpful: Milton Friedman, Ed George, Al Madansky, Rob McCulloch, Jim Press, Harry Roberts, Peter Rossi, Steve Stigler, Hodson Thornber, George Tiao and David Wallace.

3. Modesty apart, what do you consider to be your own contribution to statistics?

Modesty apart, I believe that I have done much through my research, teaching and other efforts to have the Bayesian approach be accepted in statistics and econometrics. Working many problems from several points of view and comparing solutions, mentioned earlier, has been a particularly effective approach. Further, I take great pride in my work on seemingly unrelated regression models (SURs) that have been very useful in many fields. Then, immodestly, I believe that the optimal information theoretic maximal data information priors (MDIPs) that can, with appropriate side conditions, be made invariant to relevant transformations are extremely useful. Then producing an optimal information processing procedure that yields Bayes' Theorem as a 100% efficient information processing rule and a first relation between Bayes's Theorem and entropy, as noted by Ed Jaynes, appears important to me. Also, employing this approach with other inputs has produced new, optimal information processing rules. Next, I have produced a whole range of minimum expected loss (MELO) estimates and point predictions for many problems, balanced loss functions, solutions to many applied problems including point and turning point forecasting, portfolio problems, control problems, etc. And most recently, there is the Bayesian method of moments (BMOM) that has been applied to many different models and problems and permits Bayesians to make inverse probability statements when the form of the likelihood function is unknown. Last, and very important, I take great pride in having worked with a large number of doctoral students over the years who have produced remarkable theses, most of them Bayesian, and gone on to have very successful careers in research, teaching and consulting in the U.S. and many other countries.

Given my prejudices, I would have to say my book, An Introduction to Bayesian Inference in Econometrics, Wiley, 1971, reprinted in Wiley Classics Library, 1996. It was an early report on the usefulness of Bayesian inference and provided many examples comparing Bayesian and non-Bayesian solutions to estimation, prediction, testing and control problems. Many different models, including my seemingly unrelated regression (SUR) model, were analyzed and examples computed. Much of my later work was done to extend and improve analyses that appeared in this 1971 volume, including work on estimating reciprocals, ratios, structural coefficients, and other functions of parameters in the MELO approach, extending the MDIP approach to producing priors, introducing broadened, "balanced" loss functions, etc.

4. Have you ever experienced any discrimination against Bayesianism?

No. The best that I can provide along these lines is a remark that Ted Anderson made at his 80th birthday party last June when I said he looked more like 40 than 80. He replied that you Bayesians never could count. Also, Jim Durbin once remarked that genetics determines who is Bayesian and who is not Bayesian. A few years later, he presented a Bayesian paper at a seminar at the U. of Chicago. I remarked that his genes must have changed. He remarked, "What's all this fuss about a little mathematics?"

5. For students starting out, would you recommend them to go into Bayesian statistics and, if so, what advice would you give them?

By all means I would tell them to learn Bayesian statistics and make sure that in their courses and work that they work inference and decision problems from different points of view and compare solutions. To make meaningful comparisons, they have to understand not only the Bayesian approach but others, e.g. sampling, likelihood, structural, empirical likelihood etc. approaches. In recent lecturing to grad students, I have found that many coming into my courses are all mixed up about definitions of probability, interpretations of Bayesian and non-Bayesian confidence and prediction intervals, testing procedures, etc. In an old fashioned approach to get them straightened out, I tell them that these and similar issues will be featured on the final examination. Then they get serious and learn what's what.

6. Having worked with economists for many years, do you find their views about Bayesian methods as a whole to be different from workers in other fields, e.g. physicists, engineers etc?

Economists, in contrast to workers in other fields, tend to be more familiar with the theory of decision-making under uncertainty developed by Ramsey, Savage, Friedman and others. Also, economic theorists have used Bayes's Theorem as a learning model for many years. However, many older quantitative, applied economists and econometricians have just received non-Bayesian material in their training and many tend to be wary of the Bayesian approach using arguments that have appeared in old statistics and econometrics texts. However, the new generation is much better educated in Bayesian matters and has already produced many important and useful Bayesian results. With respect to physicists, many, if not most, are not well-trained in statistics, Bayesian or non-Bayesian. Surprisingly, not very many physicists with whom I have come into contact have read Jeffreys's book, Theory of Probability, although more tell me that they will read it. Many engineers have taken courses in engineering statistics and appear to me to be quite pragmatic. They will use anything that works in practice, including Bayesian analysis, perhaps prodded along by Richard Barlow who teaches engineers Bayesian analysis at the U. of California at Berkeley.

7. What do you enjoy most about your work?

Getting solutions that work well in practice and seeing grad students succeed in their doctoral research and careers.

Grading examinations and attending "windy'" academic committee meetings.

8. What is your favourite statistics book?

H. Jeffreys's, Theory of Probability [Note that physicists tend to refer to statistics as "probability theory," perhaps because Rutherford is supposed to have said that if you need statistics to analyze your data, your experiment needs redesigning.] I also like Jim Berger's book, Statistical Decision Theory and Bayesian Analysis, 2nd ed. Springer-Verlag, 1985.

9. What is your favourite Bayesian statistics joke?

Would you want your daughter to marry a Bayesian?

10. Why did you decide to start ISBA?

Bayesian statistics had become so important as a foundation for all the sciences that many, including myself, thought it appropriate to aid its development world-wide by creating ISBA. In addition, our NBER-NSF Bayesian Seminar group had had many productive and very enjoyable meetings in Venezuela, Mexico, India, Canada, Brazil, etc. and thus the decision to extend these interactions into the future in a more organized manner was not hard.

ISBA's growth and development, particularly its successful meetings, new chapters in India, Chile and S. Africa, and enlarged membership are very impressive. Also, I particularly like the plans for the new, expanded ISBA Newsletter that have recently been circulated worldwide. The new Newsletter, it appears to me, will be one step along the way to a much needed ISBA Journal of Bayesian Analysis serving all the disciplines.

How should ISBA develop over the next few years? Are there any specific things you would like to see it do?

While there are many possibilities, including the production of an ISBA Journal of Bayesian Analysis, I would like to see ISBA do all it can to support the activities, meetings and publications of the Chapters, new and old. With respect to world meetings, it would also appear worthwhile to have volumes produced that contain the major theoretical and applied papers and general discussions of major issues presented at each meeting. If well-designed and if many relevant issues are treated, e.g. how to use Bayesian analysis to make tax and budget policy as done by Charles Whiteman in connection with his consulting with the governor of the State of Iowa, these volumes may become "best sellers" and generate revenue for ISBA and its Chapters.

11. What have been the greatest changes in Bayesian methods in the years since you started?

One is the current ability to compute almost any integral by numerical techniques, e.g. MCMC, etc. Second, we now have a plethora of procedures for producing both diffuse and informative priors. Third, and very important, we now have many more successful applications of Bayesian analysis, e.g. Mike West's impressive applied work, Jose Quintana's and Blu Putnam's very useful Bayesian portfolio formation applications, etc. Now we not only have good theory, we also have impressive performance in practice to which we can point.

I can't think of anything that has been lost by experiencing these remarkable, valuable changes.

12. What do you predict will be the changes in the next 10 years of Bayesian statistics?

Forecasting 10 years into the future is very difficult. Hence take the following with a few grains of salt. While Bayes' Theorem has been a valuable learning model for workers in all the sciences, as with all models, the Bayesian learning model will probably be generalized and changed in certain ways. Then work to evaluate the modified versions will be undertaken. Work by Diaconis, Zabell, Goldstein and myself has already resulted in new learning models that are in the process of being evaluated which should make Bayesian learning applicable to a broader range of problems and even more effective than it is today. After all, the Model-T was followed by the Model-A, the V-8 Model, etc., Newton's Laws by Einstein's Laws, etc.

13. Are we headed for a Bayesian millennium?

In my article, A Bayesian Era, read at a Valencia meeting and published in the 1988 volume, Bayesian Statistics 3, ed. J.M. Bernardo et al, I stated that a Bayesian Era has already started. Subsequent developments including the founding of ISBA and of the ASA Section on Bayesian Statistical Science in 1992 and the strong upsurge in the number of Bayesian papers and publications world-wide would lead me to say, immodestly, that I was right. Also, the benefits to society of having Bayesian statistical methods that are sound and that yield good solutions to inference, decision and control problems are enormous and deserve to be measured and reported. Congratulations to all of us who have helped make this Bayesian Era come into existence.

If you would like to read more about Professor Zellner's career, you can see another interview with him in the journal, Econometric Theory, 5, 1989, 287-317 which is reprinted in Professor Zellner's book Bayesian Analysis in Econometrics and Statistics: The Zellner View and Papers, Edward Elgar Publ. Ltd, 1997. There is also an interesting collection of 48 papers by 98 authors in honour of Professor Zellner: Bayesian Analysis in Statistics and Econometrics: Essays in Honor of Arnold Zellner, edited by Donald A. Berry, Kathryn M. Chaloner and John K. Geweke, Wiley, 1996.

In Bayesian statistics the distinction between parametric and nonparametric methods refers to inference problems in which one can assume the existence of a true, but unknown, distribution for the random elements associated with the observations. If X is the set of all possible values of observations and M is the family of all possible distributions on X (including the true one, obviously) then the prior distribution, typical of the Bayesian paradigm, is a probability law $\mu$ on a $\sigma$ -algebra of subsets of M. Usually, the statistician's knowledge leads him/her to the definition (possibly unaware) of a function $\tilde{\theta}$ from M onto $\Theta$ and a family of distributions M = {p : $\theta$ $\in$ $\Theta$ } such that $\mu$ ({p}| $\tilde{\theta}$ = $\theta$ ) = 1 for any $\theta$ in $\Theta$ . In other words, $\tilde{\theta}$ plays the role of a sufficient statistical parameter: given the value of $\tilde{\theta}$ , say $\theta$ , any further information is useless in determining the true law (assumed, in that case, coincident with p). Usually, $\Theta$ is a subset of a Euclidean space, whose dimension is relatively small, and, therefore, it is convenient to implement the Bayesian paradigm on both the (prior) distribution on $\tilde{\theta}$ and the statistical model M . As obvious, the statistical methods based on that approach are called parametric, whereas the term nonparametric refers to those methods in which $\mu$ is defined directly on M, without the intermediate use of parameters. Though more inherent to the Bayesian paradigm than the parametric formulation, the nonparametric one has attracted the interest of Bayesian only after the classical work A Bayesian analysis of nonparametric problems by Thomas S. Ferguson, published in the Annals of Statistics in 1973. An explanation for the late interest rests upon the conceptual difficulty in determining a distribution on M and the practical difficulty in defining and dealing with prior distributions on infinite dimensional spaces.

Historically, the nonparametric viewpoint goes back, at least, to the famous paper La prévision ses lois logiques, ses sources subjectives by Bruno de Finetti, published in the Annales de l'Institut Henri Poincaré in 1937, which contains a series of lectures given by the author at that Institute in May 1935. More precisely, the fundamental representation theorem of exchangeable laws itself is presented in a nonparametric form: a sequence of real-valued random variables is exchangeable if and only if its probability distribution can be represented as $\int_{M}^{}$ p $\mu$ (dp) for an adequate choice of $\mu$ . The theorem, as given by de Finetti, and its proof, although the natural extension of the already known result for events, were a cumbersome task in 1935-1937. It is noteworthy that de Finetti fulfilled that task, because of a convenient metrisation of M and an adequate definition of the integral on M, which came, at least, twenty years before the appearance of the general theory of weak convergence on metric spaces, due to Yuri V. Prokhorov.

A more direct link to the modern concept of Bayesian nonparametric statistical method can be found in a short note presented by de Finetti in 1934 at the XXIII meeting of the Società Italiana per il Progresso delle Scienze, published in 1935 with the title Il problema della perequazione [reprinted in Bruno de Finetti, Scritti (1931-1936), Pitagora Editrice, 1991]. In that paper the author shows, in a clear but very concise form, that the problem of fitting observations [loose translation of ``perequazione''] can be conceptually solvable by means either of a technique based on a nonparametric Bayesian estimation of the true law (the estimator being a mean value of the posterior distribution on M) or the predictive distribution of a future observation. The major flaw in the paper by de Finetti consists of the actual lack of examples of distributions on M which can show the practical implications of his ideas and be ``useful'' from a statistical viewpoint. Such goals were achieved only four decades later when Ferguson studied the extension of the Dirichlet distribution to M and analysed its application in Statistics.

Generally, I think it is safe to say that most people are introduced to statistical inference from a frequentist perspective. Many students in graduate programs in statistics are not taught the basics of the Bayesian paradigm including subjective probability, the modeling of prior beliefs through probability distributions, and posterior and predictive inference. This lack of exposure of students to Bayesian thinking has had a major impact on the growth of the use of Bayesian methods in areas of application. I think it is important to discuss how we can effectively communicate Bayesian ideas in academic and industrial settings and how we can facilitate the spread of Bayesian thinking among applied statisticians. This column will discuss a variety of issues relevant to teaching Bayesian ideas. Here is a short list of some topics, although other suggestions by ISBA members are certainly welcome.
$\bullet$ How can one introduce Bayesian thinking in the standard elementary statistics class taught as a service course to non-math majors?
$\bullet$ Is it desirable to teach both Bayesian and frequentist thinking in an introductory class?
$\bullet$ What topics should be taught in an introductory applied Bayesian course at the graduate level?
$\bullet$ What is the role of software in teaching Bayesian inference?
$\bullet$ What are effective short courses for teaching Bayesian methodology?
$\bullet$ How do we argue that Bayesian inference is superior (at least in some settings) to frequentist inference?
In future columns, I hope to describe Bayesian courses that are currently offered and give reviews of recently published texts and software that will help in communicating Bayes.

Recently (August 1997) a series of articles appeared in the American Statistician on the desirability of introducing Bayesian thinking in the first introductory statistics class taught as a service course to students outside of the mathematics or statistics department. I have taught introductory statistics for over 20 years and I have found it very difficult communicating frequentist thinking to this audience. The notion of a sampling distribution is obscure to students and, more importantly, the students have a hard time understanding the repeated sampling interpretation of confidence. I think Bayesian thinking is very attractive at this level. Thinking in terms of subjective probability is very natural for students and Bayes rule can be taught as a formal mechanism for the natural process of updating one's beliefs about unknowns when more information is observed. Currently Alan Rossman and I are writing a beginning statistics text that introduces inference from a Bayesian viewpoint. The first half of the test focuses on data analysis. The second half introduces probability basics -- the focus is on the interpretation of discrete probability tables for one and two variables. Inference is first introduced for categorical models using Bayes rule. A two-way table, a ``Bayes box" (hypothetical counts classified by models and data), is used to teach Bayes rule. We then have chapters on inference for one and two proportions and a normal mean. We focus on the use of discrete priors for unobservables, since these are easier to assess and interpret and it avoids any use of calculus. One innovative aspect of this text is that it essentially is a collection of activities that students work on in class in small groups. Currently we are using this material at Bowling Green for five sections a semester. I think the course is successful in getting across the big ideas in inference (population, samples, confidence in making inference) and the student is well prepared to take a second statistics class on methodology. Unfortunately, there are very few elementary statistics texts currently available that use Bayesian thinking. One text of note is Don Berry's text ``Statistics: A Bayesian Perspective". I have used Don's text both at Duke and Bowling Green with success and I would recommend it for anyone who is teaching a one semester introductory class. Don's text has been very influential in my thinking about teaching elementary Bayes.

This software provides Fortran 90-Splus functions and Matlab programs to fit Time-Varying Autoregressive (TVAR) models with a fixed order using a Bayesian Dynamic Linear Model (DLM) framework. The evolution of parameters in time is defined through a Random Walk and with standard Normal-Inverse Gamma priors on model coefficients and variances. Using a DLM algorithm for sequential and retrospective smoothing, the software computes posterior estimates of the AR coefficients and variances at each time. The main focus is on inference on latent component structure defined through the time-varying characteristic roots of the process.

Basically, the software is organised in three parts and particularly for the Fortran-Splus version each one is defined as follows. A program named "grid.90", computes the log-likelihood and mean square error (MSE) of a DLM-TVAR for a desired grid of values for model order and discount factors for the evolution of parameters. As output, the combination of order-discount factors that maximises the log-likelihood and minimises the MSE are reported and both can be used for further analysis. The second part consists of a program named "tvar.90" which fits the TVAR model for any specification of model order and discount factors. "tvar.90" computes and saves posterior means for model parameters and has the additional option of generating samples from the posterior distribution of the parameters at each time. Finally, the program named "decomp.90", reads the output for "tvar.90" and produces posterior inference on latent component structure with component ordering via the amplitude, periodicity or corresponding moduli for characteristic-component roots. Furthermore, the Splus functions summarise the output of the Fortran programs offering different graphical displays. Some involve plots of the data with the time-varying latent components, time trajectories for moduli and frequency of the associated characteristic roots which can also include 95 % posterior intervals at selected time points.

The software is designed for a Unix environment and the Fortran version requires LAPACK, public domain software for matrix algebra. On the other hand, the MATLAB version is free-standing and permits time-varying spectral inference. The software comes with 3 data sets and tutorial examples. The data sets are: an Electro-Encephalogram (EEG) trace, an oxygen-isotope time series and a series of non-stationary sea level pressures. Particularly for the EEG data, the Matlab version offers the possibility of contour/surface plotting of the EEG channels and related inferences over a graphical representation of the head of a person. As the authors of the software point out, the Matlab version requires further upgrading.

This a very general software for modelling time series within a linear-non stationary context that incorporates model selection through a formal likelihood exploration. Although all the emphasis is in latent structure rather than aspects as forecasting, it offers a diversity of analysis for component modelling that range from simple computation of posterior moments to exploration via direct Monte Carlo simulation of posterior distributions. Additionally, component structure can be studied with a variety of orderings so properly handles for identifiability issues that naturally arise in DLM-TVAR models. The software is free, occupies approximately 99 Kb of disk space and is available to the public domain from

Patrick Bajari's thesis, "The First Price Auction with Asymmetric Bidders: Theory and Applications" uses new methods in economic theory and Markov Chain Monte Carlo to tackle an old problem: developing an econometric model of competitive bidding. Understanding competitive bidding is a question of considerable practical importance. Worldwide, the construction industry is a 3.2 trillion dollar market (Engineering New-Record, 11/30/98) where a large percentage of contracts are awarded by some form of competitive bidding. The particular market considered in his research was bidding by construction firms in Minneapolis, Minnesota for contracts to repair and build highways. The methodology, however, could be applied to study bidding in other procurements. The research addressed three questions. First, what is the (unobserved to the econometrician) cost structure of each firm in the market. Second, what is the posterior probability that the market is competitive or collusive. Third, what are the econometrician's posterior beliefs about bidding if different rules were used in awarding the contracts.

Bayesian methods are well suited to study these problems. After considerable research into the cost structure of the market, the author was able to develop restrictions on the space of parameter values in the firm's cost structures. The prior had compact support, that is, some parameter values were given zero prior probability (a trivial example is that trucking costs should not be negative and can be bounded above by using market prices for rental rates on trucking). Also, the support of the likelihood function depends on model parameters, greatly complicating the study of the asymptotic properties of maximum likelihood estimation. This poses no particular difficulty for Bayesian methods, however.

Economic theory is used to construct the likelihood function used in this research. Equilibrium models of strategic bidding generate a probability density for the bids that firms submit, condition on the model parameters (which characterized the firm's cost structures). The economic theory of equilibrium imposes two assumptions: first, firms have correct beliefs about the probability distribution of bids for all other firms and second, firms maximize expected profit.

In this research, a panel data set of bidding by 21 firms for 52 contracts was constructed. Data on project specific variables that enter a firm's cost (for instance, the distance of each firm from the project) was also collected. For a fixed set of parameter values, the equilibrium was computed for each bid in each project. The likelihood function is then the product of the probability density function evaluated at each bid in each project.

Conditional upon the observed bids and project specific cost variables, the posterior distribution of model parameters was simulated by using a Metropolis-Hastings algorithm. Several models were considered: a model of competition, a model of collusive bidding, and models with unobserved, project specific heterogeneity. Using the simulation output, it was straightforward to compute marginalized likelihoods for each model. Bayes factors were used to decide between the non-nested models in this research. The preferred specification was a model of competition with unobserved, project specific heterogeneity.

Using the posterior simulator, the econometrician's posterior beliefs about functions of the model parameters can be explored. The posterior distribution of firm's markups implied that the market was highly competitive. The posterior mean was between 1 and 7 percent for most projects. Bayes factors overwhelming favored the competitive specifications over the collusive specifications. Prior robustness analysis was conducted to study the sensitivity of these conclusions to the prior and in the class of priors with the same compact support, the results were not terribly sensitive.

A last question this research studies was to compare alternative rules for awarding the contract. The standard rule in bidding for government contracts is to award the contract to the lowest responsible bidder and the lowest bid. An alternative mechanism discussed among economists, with highly attractive theoretical properties, is a second price auction. Here, the firm with the lowest bid is still awarded the contract, but is paid the amount of the second lowest bid. For each construction project, the econometrician's posterior expectation of the cost to the government under both rules was computed. This research found that the second price auction results in a lower expected cost to the government, saving, on average, between 2 and 5 percent. Readers interested in reading this research can find it on the author's web page. .

The list below is certainly not exhaustive, and may not even be representative. Our intended goal here is to give the readers a sense of the current use of Bayesian analysis in Genetics.
$\bullet$ J.S. SINSHEIMER, J.A. LAKE, AND R.J.A. LITTLE (1996). Bayesian Hypothesis Testing of Four-Taxon Topologies Using Molecular Sequence Data. Biometrics 52, pp193-210.
Contact: Roderick J. A. Little, University of Michigan, <rlittle@umich.edu>.

Bayesian analysis is used to test three hypotheses concerning the correct topology from the available DNA sequences. Classical hypothesis testing is reported to be difficult due to test the multiple alternative hypotheses involved, while Bayesian analysis being conceptually straightforward. Multinomial and Multivariate normal sampling models are used with uniform priors on the parameters to obtain the posterior probabilities of the hypotheses. Using a large simulation study to assess the frequentist properties of the Bayesian tests, the authors conclude that Bayesian tests are well calibrated and have reasonable discriminating power for a wide range of realistic conditions.
$\bullet$ K. SJ¨OLANDER, K. KARPLUS, M. BROWN, R. HUGHEY, A. KROGH, I. S. MIAN AND D. HAUSSLER (1996). Dirichlet mixtures: a method for improved detection of weak but significant protein sequence homology. Computational Applications in Bio. Science 12, pp 327-345
Contact: Kimmen Sjölander, University of California, Santa Cruz, <kimmen@cse.ucsc.edu>.

Bayesian methods are used in finding remote homologs with lower primary sequence identity . The authors use a Dirichlet mixture prior for amino acids distributions. This prior is estimated using data obtained from multiple alignment databases on observed counts of amino acids in clusters, and the maximum likelihood method. The observed amino acid frequencies are then used to obtain posterior estimates of amino acid probabilities at each position in a profile.
$\bullet$ I. HOESCHELE, P. UIMARI, F. E. GRIGNOLA, Q. ZHANG, K.M. GAGE (1997). Advances in Statistical Methods to Map Quantitative Trait Loci in Outbred Populations. Genetics 147, pp1445-1457.
Contact : Ina Hoeschele, Virginia Polytechnic Institute and State University, <ina@vt.edu>.

Six different statistical methods, including maximum likelihood, exact and approximate Bayesian methods, used for gene mapping are reviewed. Authors comment that Bayesian analysis takes full account of the uncertainty associated with all unknowns in the problem, and allows fitting of different models quantitative trait loci variation. References to many other related work using Bayesian methods are also given.
$\bullet$ P. UIMARI AND I. HOESCHELE (1997). Mapping-Linked Quantitative Trait Loci Using Bayesian Analysis and Markov Chain Monte Carlo Algorithms. Genetics 146, pp735-743.
Contact: Ina Hoeschele, Virginia Polytechnic Institute and State University, <ina@vt.edu>.

A Bayesian analysis for mapping linked quantitative trait loci(QTL) using multiple linked genetic markers is given. This approach was motivated by the evidence of detecting a single ``ghost QTL'' with least square analysis, when in fact two linked QTL were segregating. Here, the authors extend existing Bayesian linkage analysis to fit models that allow multiple linked markers; specifically zero, one and two QTL linked to the markers. Model selection from among these linkage models is done using data simulated under four different designs with map positions and effects. Three different MCMC algorithms are used to fit a mixed effect model for each data. These MCMC algorithms use different methods of fitting, such as use of indicator variable in the model, variable selection approach, and reversible jump MCMC. All three MCMC methods are found to do well. Detailed comparisons of these methods are given. The authors conclude that it is feasible to fit linked QTL simultaneously using Bayesian analysis, and that it provides estimates of all genetic parameters and can fit alternative QTL models.
$\bullet$ R. L. DUNBRACK, JR. AND F. E. COHEN (1997). Bayesian statistical analysis of protein side-chain rotamer preferences. Protein Science, 6, pp1661-1681.
Contact: Roland L. Dunbrack, Jr., Institute for Cancer Research, Philadelphia. <rl_dunbrack@fccc.edu>.

A Bayesian analysis is used to account for varying amount of information in the Protein Data Bank for $\chi_{1}^{}$ backbone dependent rotamer distributions, and to obtain more complete estimates of these distributions. In addition, Bayesian analysis is used to provide better estimates of the probability of occurrences of other rare rotamers. Multinomial models and Dirichlet priors are used. Parameters of the prior distribution are derived from previous data or from pooling some of the present data. Model checking is done using a Bayesian version of p-value calculated by simulating both parameter and data.
$\bullet$ G. PARMIGIANI, D. A. BERRY AND O. AGUILAR (1998). Determining Carrier Probabilities for Breast Cancer Susceptibility Genes BRCA1 and BRCA2. American Journal of Human Genetics, 62, pp145-158.
Contact: Giovanni Parmigiani, Duke University, < gp@stat.duke.edu.edu>.

Breast cancer susceptibility genes BRCA1 and BRCA2 have recently been identified on the human genome. Women who carry a mutation of one of these genes have a greatly increased chance of developing breast and ovarian cancer, and they usually develop the disease at a much younger age, compared with normal individuals. Women can be tested to see whether they are carriers. A woman who undergoes genetic counseling before testing can be told the probabilities that she is a carrier, given her family history. In this paper we develop a model for evaluating the probabilities that a woman is a carrier of a mutation of BRCA1 and BRCA2, on the basis of her family history of breast and ovarian cancer in first- and second-degree relatives. Of special importance are the relationships of the family members with cancer, the ages at onset of the diseases, and the ages of family members who do not have the diseases. This information can be elicited during genetic counseling and prior to genetic testing. The carrier probabilities are obtained from Bayes's rule, by use of family history as the evidence and by use of the mutation prevalences as the prior distribution. In addressing an individual's carrier probabilities, we incorporate uncertainty about some of the key inputs of the model, such as the age-specific incidence of diseases and the overall prevalence of mutations. There is some evidence that other, undiscovered genes may be important in explaining familial breast cancer. Users of the current version of the model should be aware of this limitation. The methodology that we describe can be extended to more than two genes, should data become available about other genes.
$\bullet$ G. M. PETERSEN, G. PARMIGIANI AND D. THOMAS (1998). Missense Mutations in Disease Genes: A Bayesian Approach to Evaluate Causality. American Journal of Human Genetics, 62, pp1516-1524.
Contact: Gloria M. Petersen, Johns Hopkins University, < gpeterse@jhsph.edu>.

The problem of interpreting missense mutations of disease-causing genes is an increasingly important one. Because these point mutations result in alteration of only a single amino acid of the protein product, it is often unclear whether this change alone is sufficient to cause disease. We propose a Bayesian approach that utilizes genetic information on affected relatives in families ascertained through known missense-mutation carriers. This method is useful in evaluating known disease genes for common disease phenotypes, such as breast cancer or colorectal cancer. The posterior probability that a missense mutation is disease causing is conditioned on the relationship of the relatives to the proband, the population frequency of the mutation, and the phenocopy rate of the disease. The approach is demonstrated in two cancer data sets: BRCA1 R841W and APC I1307K. In both examples, this method helps establish that these mutations are likely to be disease causing, with Bayes factors in favor of causality of 5.09 and 66.97, respectively, and posterior probabilities of .836 and .985. We also develop a simple approximation for rare alleles and consider the case of unknown penetrance and allele frequency.
$\bullet$ J. S. LIU AND C. E. LAWRENCE (1999). Bayesian Inference on biopolymer models. Bioinformatics, 15, pp38-52.
Contact: Jun S. Liu, Stanford University, <jliu@stat.stanford.edu>.

This article introduces the Bayesian methods and its use to researchers in bioinformatics. The article gives a tutorial introduction to Bayesian methods using an example involving data from tossing two different coins. This example is then further extended to illustrate application in bioinformatics using two specific examples: sequence segmentation and global sequence alignment. The authors state that the need for setting parameter values has been the subject of much discussion, and that a distinct advantage of the Bayesian method is the added modeling flexibility in the specification of parameters. The authors comment that the rich history of computation in bioinformatics such as dynamic programming recursions can be modified to complete the high dimensional computation required by the Bayesian methods, and that through the use of these recursions, the full power of the Bayesian methodology can be brought to bear on a wide range of problems previously addressed by dynamic programming.

It is wonderful to have this new edition of the ISBA newsletter before us. One of the sections that we plan to have in the new edition is called the ``Student's Corner''. We are hoping to include in this section abstracts of dissertations of students who are currently doing research (like those from ISDS, right after this note). This would not only serve as a common platform where students can interact with one another on common problems, but would also serve as a good indicator for the fresh students as to what the current trends in (Bayesian) statistical research are. Students are welcome to discuss other problems that they may have come across in their academic or professional work. We would also like to include plans and suggestions for common activities such as meetings or creating web-pages where we might post discussions on problems that we face. These problems would cover theoretical and methodological issues as well as problems on Bayesian Data Analysis. Some of the most widely faced problems by the modern day statistician pertains to software related issues. Since heavy computing is an indispensable technology for analysing models in the Bayesian framework, many students have queries on such issues. We feel that the student corner can serve as that much sought after platform where students can exchange their thoughts and experiences on such issues. We would like to seek your co-operation in making the ``Students' Corner'' a really useful section in the newsletter. We plan to have most of our communications through e-mail. We would particularly encourage students to send their comments on what they would like to have in this section and their suggestions on what we can do to improve this section.

1998 Ph.D's at
ISDS, Duke University

(Dissertations available at
 
www.isds.duke.edu/people/alumni.html).

A statistical framework is proposed to automate image feature identification and therefore facilitate the image understanding tasks of registration and segmentation. Features are delineated using an atlas image, and a probability distribution is defined on the locations and variations in appearance of these features in new images from the class exemplified by the atlas. The predictive distribution defined on feature locations in a new image from the class essentially balances the two notions that, while each individual feature in the new image should appear similar to its atlas representation, contiguous groups of features should also remain faithful to their spatial relationships in the atlas image. A joint hierarchical model on feature locations facilitates reasonable spatial deformations from the atlas configuration, and several local image measures are explored to quantify feature appearance. The hierarchical structure of the joint distribution on feature locations allows fast and robust density maximization and straightforward Markov Chain Monte Carlo simulation. Model hyperparameters can be estimated using training data in the form of manual feature observations.

Given Maximum posteriori estimates an analysis is performed on in vitro mouse brain Magnetic Resonance images to automatically segment the hippocampus. The model is also applied to time-gated Single Photon Emission Computed Tomography cardiac images to reduce motion artifact and increase signal-to-noise.

Multiple EEG signals recorded under different ECT (electroconvulsive therapy) conditions are analyzed using TVAR models. Decompositions of these series and summaries of the evolution of functions of the TVAR parameters over time, such as characteristic frequency, amplitude and modulus trajectories of the latent, often quasi-periodic processes, are helpful in obtaining insights into the common structure driving the multiple series. From the scientific viewpoint, characterizing the system structure underlying the EEG signals is a key factor in assessing the efficacy of ECT treatments. Factor models that assume a time-varying AR structure on the factors and dynamic regression models that account for time-varying instantaneous lead/lag and amplitude structures across the multiple series are also explored. Issues of posterior inference and implementation of these models using Markov chain Monte Carlo (MCMC) methods are discussed.

Decompositions of the scalar components of multivariate time series are presented. Similar to the univariate case, the state-space representation of a VAR(p) model implies that each univariate element of a vector process can be decomposed into a sum of latent processes where every characteristic modulus and frequency component appears in the decomposition of each univariate series, while the phase and amplitude of each latent component vary in magnitude across the univariate elements. Simulated data sets and portions of a multi-channel EEG data set are analyzed here in order to illustrate the multivariate decomposition techniques.

In Bayesian inference an imprecise input is typically represented by a class of probability measures on the unknown quantities. In the previous literature about parametric robustness most of the efforts have been concentrated on classes of prior distributions for a finite collection of unknown parameters, while the distribution of the data conditionally on those parameters is considered to be known exactly. Robustness with respect to misspecification has received less attention due to the mathematical difficulties. In the nonparametric context the two aspects are no longer separated but they are in fact identified with the presence of a unique unknown infinite-dimensional parameter.

We examine here nonparametric analysis within the context of Exchangeable Tree processes that fall in the general class of exchangeable processes and represent a subclass of Tail-free processes. Exchangeable Trees constitute indeed a general class of processes and contain as particular cases Dirichlet processes and Polya Trees, two of the most used nonparametric priors. We propose a predictive interpretation for an imprecise prior input that leads us to formulate a general solution for the global robustness investigation. We are then able to quantify the range of linear functionals of the conditional predictive distribution after some data have been collected.

The larger framework implied by enlarging the scope to an infinite-dimensional parameter leads us to expect less robustness. Some annoying phenomena, like dilation, are experienced, deviating from the usual pattern in parametric robustness. We are able to compare how this is affected by the prior inputs and quantify how robustness can be improved by restricting attention to particular subclasses.

Finally, a different problem is approached: simulation from mixture distributions whose components are supported on spaces of different dimensions. Here a novel approach is considered by reducing the problem to that of simulating from a single target distribution that is absolutely continuous with respect to the Lebesgue measure on the largest support of the components. This approach is suggested from an alternative representation of the simulation goal in the simplified situations when the mixture consists only of a one-dimensional component and a degenerate component. Tools for suitable generalization to arbitrarily nested components and to an arbitrary number of them are provided. Hence two alternative methods are derived and one of them is successfully employed in analyzing simulated data as well as a real data set. The new approach is designed in order to avoid the numerical integration needed for evaluating the relative weight of each component and represents in the case of nested components an alternative to the currently available MCMC methods such as the reversible jump algorithm (Green, 1995) and the composite-space approach (Carlin and Chib, 1995). The distinguishing feature of the proposed method is the absence of proposals for jumping between components of different dimension or of the specification of pseudopriors. This allows for a more automatic implementation. Furthermore it is argued that in the actual implementation of a Markov chain that simulates from the absolutely continuous target distribution one can automatically build up a chain that allows for moving from one component to any other possible component which possibly improves the speed of convergence. Finally, in order to assess the mixing behaviour, standard convergence diagnostics for absolutely continuous stationary distributions can be used.

"Well, I have great respect for Bruno de Finetti and Harold Jeffreys, but you see Luis, here we are in the 10th floor of Evans Hall over viewing the Golden Gate. Bayesians have some work in the basement". Those were the words of J. Neyman to Luis Raúl Pericchi in 1978. Luis finished his Master and went to Imperial College in London to work on a PhD under the supervision of A.C. Atkinson, working on a thesis on Information Theory together with Bayesian and Likelihood statistics for data transformations and model selection. Once he finished it he went back to Caracas, Venezuela, to work at Universidad Simón Bolívar were he started a Bayesian group.

The whole idea was motivated by the presence at USB, while Luis was a undergraduate student in the early seventies, of Ignacio Rodríguez Iturbe, who took a PhD in M.I.T. and went back to his home country to work at USB and the Instituto Venezolano de Investigaciones Científicas; he was very interested in the possible applications of Bayesian statistics to hydrology, in particular, in how to enlarge limited information in some river basins, using regional information from other sites nearby. Ignacio motivated Luis Raúl and other students to work on Bayesian statistics and later made important contributions to its applications to hydrology. He nowadays holds a chair in Princeton, but he still is an influential figure in Venezuela.

The work in the basement started for Luis Raúl with a stats lab that was called TAE, "Taller de Estadística", within the Maths Department at USB. In the eighties TAE was a gathering place for statisticians and several students got interested in Bayesian statistics. I remember struggling with the 70Mb of disk space and the 8Mb of RAM memory of our brand new Sun 3/110 workstation to squeeze in the code of Bayes 4 that Allan Skene brought us from Nottingham and the revolutionary New S that William Nazaret gave us from AT&T. Few years later, in '92, several faculty members of the areas of Numerical Analysis and Mathematical Programming, approached TAE, with the idea of forming a more comprehensive centre. A four years grant of 800,000 dollars from the Venezuelan Government, sponsored by the Interamerican Bank of Development, led to the creation of CESMa (Centro de Estadistica y Software Matematico), under the directorship of Marianela Lentini. The same year we organised one of Zellner's meetings on Bayesian statistics and econometrics, which was well attended by many statisticians from the Americas and left some people pondering the good properties of the añejo distribution for a while. The latest development of our group occurred in 1996 when the USB decided to form a new Department called "Department of Scientific Computing and Statistics", enhancing the importance of statistics within the university.

@ Our group

The hard core of Bayesians at USB is made of the following people:

$\bullet$ Víctor De Oliveira (vdo@cesma.usb.ve). Spent a year as a postdoc at the Institute of Statistical Sciences after taking a PhD at the University of Maryland College Park under the supervision of Benjamin Keden. He joined us in September '98 and works in spatial problems and geostatistics.

$\bullet$ María Eglée Pérez (eglee@cesma.usb.ve). Finished her PhD at Universidad Central de Venezuela in 1994 under the supervision of Luis Raúl Pericchi. She works in problems related to inference for the Exponential Family, Bayesian analysis of discrete data and biostatistics. She has a wonderful voice, well known among the public of the cabaret at the last Valencia.

$\bullet$ José Miguel Pérez (jperez@cesma.usb.ve). He arrived to USB in September '98, from Purdue University where he finished a PhD under the supervision of Jim Berger. He works in methods related to automatic priors in particular mixture models with applications to the clustering and characterisation of variables.

$\bullet$ Luis Raúl Pericchi (pericchi@cesma.usb.ve). Took his PhD at Imperial College in 1981. He is the most senior member of our group and his work these days is very much focused on model comparison, with particular interest in non subjective priors, for which he and Jim Berger developed the Intrinsic Bayes Factor. His academic activities span a wide range of topics including applications to medical statistics, engineering, econometrics and official statistics. He is known as a player of Brazilian guitar.

$\bullet$ Raquel Prado (raquel@cesma.usb.ve). She is back from North Carolina since September last year; there she took her PhD at the Institute of Statistics and Decision Sciences under the supervision of Mike West. She works in non stationary time series and applications of Bayesian methods to signal processing.

$\bullet$ Bruno Sansó (bruno@cesma.usb.ve). I finished my PhD at Universidad Central de Venezuela in 1992 as a student of Pericchi working on Bayesian robustness and spent some time at the University of Liverpool under the supervision of Phil Brown. I now work on spatio-temporal models with particular interest in environmental variables.

Other colleagues, with different degrees of Bayesianism share our statistical activities, they are:

$\bullet$ Lelys Guenni, PhD Griffith University, 1992. Spatio-temporal models for environmental variables, stochastic hydrology. In her last talk she promised that it was her last non-Bayesian one!

$\bullet$ Raúl Jiménez, PhD Universidad Central de Venezuela, 1992. Asymptotic behaviour of stochastic processes, theory of statistical information.

$\bullet$ Isabel Llatas, PhD Wisconsin-Madison, 1987. Experimental design, multivariate statistics, statistical quality assessment. She is currently the director of CESMa.

$\bullet$ José Luis Palacios, PhD Berkeley 1982. Random walks on graphs, discrete stochastic processes, combinatorics.

$\bullet$ Adolfo Quiroz, PhD MIT 1986. Nonparametric methods, goodness of fit for multivariate data.

$\bullet$ Leonardo Saab, Master Wisconsin Madison, 1985. Statistical applications of quality management, growth curves for Venezuelan children.

@ Working environment

USB has a very pleasant campus in the outskirts of Caracas, the capital of Venezuela. The weather is fairly mild thanks to the 1,000 plus meters above the sea level and the low density of the urban development around the campus. Tropical gardens, that are part of the university's pride, surround the buildings and create a very pleasant compare our campus to a resort!

At CESMa we work mainly with Sun workstations, at last count they were around 16, the last arrival being a powerful 450 Enterprise with two processors. We have the tradition of naming them after characters of the Latin American literature and, as a result, we have a colourful network populated with thieves, whores, heroes, fantastic people created by the imagination of García Márquez, Vargas Llosa and the like.

We have around 20 students following courses in three programmes: Diploma Master and PhD. All three programmes are quite new: they have been in place for less than two years, nevertheless they already seem to be a success.

We have definitely climbed some steps up from the basement during the last years and in spite of the uncertainties that we live these days in our country, I think that the future is bright for Bayesian statistics in Venezuela. This article is available in html with links and pictures at www.cesma.usb.ve/novedades/isbae.html .

Workshop on Bayesian Nonparametric Statistics. July 23-28, 1999. Reading University, UK. The Workshop is being organised by the Research Section of the Royal Statistic Society and funded by the Engineering and Physical Sciences Reseach Council (EPSRC). Well-known researchers and leaders in the field have been invited to speak on methods, theory and applications.

INFO: http://www.stats.bris.ac.uk/~guy/Research/Workshop.html

MaxEnt '99. August 2-6, 1999. Boise State University, Boise, Idaho. MaxEnt '99 is the Nineteenth International Conference on Maximum Entropy and Bayesian Methods. There is no explicit deadline for sending an abstract.

INFO: http://www.maxent99.boisestate.edu/

1999 NBER/NSF Time Series Conference. August 23-25, 1999. Academia Sinica, Taipei, Taiwan. The deadline for the submission of an abstract is June 1, 1999.

INFO: http://www.sinica.edu.tw/econ/nber-nsf/

Case Studies in Bayesian Statistics, Workshop 5. September 24-25, 1999. Carnegie Mellon University, Pittsburgh, Pennsylvania. The Workshop aims to explore the interplay between statistical theory and practice in the context of concrete and collaborative research projects.

INFO: http://lib.stat.cmu.edu/ bayesworkshop/Bayes99.html

@ Internet Resources

* The ISBA Web Page. We remind you of the ISBA Web Page. It gives information on the ISBA activities, as well as links to Bayesian resources.

URL: http://www.bayesian.org/

* Bayesian Abstract Archive. A searchable interactive archive of abstracts of papers, books, theses, and conference presentations is available at the Institute of Statistics and Decision Sciences at Duke University. The Archive is sponsored by ISBA and SBSS. The Editor of the Archive encourages you to submit your abstracts, so that it becomes a valuable resource to all Bayesians.

URL: http://www.isds.duke. edu/isba-sbss/

Bayesian Model Averaging. You can browse this document at the web site of AT&T Research. It contains downloadable S-PLUS code that implements BMA for various classes of widely used regression models, a list of papers on BMA and a list of people interested in BMA.

URL: http://www.research.att.com/~volinsky/bma.html

@ Research Opportunities

Fifth Framework Programme. The Fifth Framework Programme (FP5) sets out the priorities for the European Union's research, technological development and demonstration activities (RTD) for the period 1998-2002, with an agreed budget of 13,700 million Euro.

Four focussed Thematic programmes and three wide ranging Horizontal programmes have been defined. The Thematic Programmes have the following names: ``Quality of life and management of living resources'', ``User-friendly information society'', ``Competitive and sustainable growth'', and ``Energy, environment and sustainable development''.

Calls for proposals for RTD will be issued within each Programme. The first call has been launched last March.

FP5 is open to all legal entities established in the Member States of the EU and in any of the other States associated to the Programme. Participation to ``third countries'' is governed by common conditions which are applied throughout FP5.

FP5's homepage is http://www.cordis.lu/fp5/home.html. Some documents are available for download in MS Word 6 or PDF.

@ Awards and Prizes

* Third Mitchell Prize. The Mitchell Prize is named for Toby J. Mitchell, who made incisive contributions to statistics, especially in biometry and engineering applications.

The Prize will be announced and presented at the August 1999 Joint Statistical Meetings in Baltimore and will be awarded in recognition of an outstanding paper describing how a Bayesian analysis has solved an important applied problem.

Beginning this year, the Prize will be awarded annually under the cosponsorship of the ASA Section on Bayesian Statistical Science (SBSS), the ISBA, and the Mitchell Prize Founders' Committee.

To be eligible for the 1999 Prize, a paper will either have appeared in a refereed journal or refereed conference proceedings since January 1 1997, or be scheduled for future publication in a refereed outlet. Submissions should be received by May 15, 1999.

INFO: http://www.stat.duke.edu/sites/mitchell.html

@ Miscellanea

First Mexico Workshop on Bayesian Statistics. September 9-12, 1998. IIMAS-UNAM, Mexico City, Mexico. This workshop, the first of its kind in Mexico, was held at the Universidad Nacional Autónoma de México (UNAM) and was organised by the Mexican Statistical Association (AME), two Institutes of the UNAM (IIMAS and IMATE), and the Autonomous Technological Institute of Mexico (ITAM). The programme consisted of three short courses (Decision Theory, Bayesian Inference, and Computational Methods) and two research sessions, one of which was devoted to applications.

DEBUG. In September 1998 a German BUGS user group (DEBUG - Deutsche BUGS-User-Gruppe) was founded. The group has the following aims: to provide a forum for exchange of experiences with the program BUGS; to promote exchange of examples; to bundle the interests of the users when repair and further development are discussed; to provide guidance through the literature.

INFO: http://userpage.ukbf. fu-berlin.de/~debug/

Workshops on Risk at RSS99. July 12-15, 1999. University of Warwick, Coventry, UK. The deadline for submission of contributed papers at the International Conference of the Royal Statistical Society has passed, however contributions to the scheduled specialized workshop are welcome. Two of these workshops are related to Risk Assesment.

INFO: http://www.warwick.ac.uk/statsdept/rss99/workshop.html

There are many benefits associated with joining the society not the least of which is the subscription to the ISBA Quarterly Newsletter. Please complete this form and return it with your membership fee to:

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