ABS07 - 2007 Applied Bayesian Statistics School


Pavia, Italy

3 - 7 September, 2007


Last update: 31/8/2007


The Applied Bayesian Statistics summer school has been organised since 2004 by

    Istituto di Matematica Applicata e Tecnologie Informatiche, Consiglio Nazionale delle Ricerche
    Dipartimento di Economia Politica e Metodi Quantitativi, Università di Pavia
  • This year the school is supported by the Dipartimento di Matematica, Università di Pavia.

    The school aims to present state-of-the-art Bayesian applications, inviting leading experts in their field. Each year a different topic is chosen. Past editions were devoted to Gene Expression Genomics, Decision Modelling in Health Care and Spatial Data in Environmental and Health Sciences.

    The school is open not only to post-docs and students in Statistics, but also to researchers (not necessarily from academia) from different fields, with an interest in statistical applications. Although a concise introduction to Bayesian methods is provided each year, a basic knowledge of statistical methods is advisable.

    The school makes use of lectures, practical sessions, software demonstrations, informal discussion sessions and presentations of research projects by school participants.


    The topic chosen for the 2007 school is Bayesian Methods and Econometrics.

    The lecturer will be Siddhartha Chib, Harry C. Hartkopf Professor of Econometrics and Statistics, Washington University in St. Louis, USA.

    He will be assisted by Luca La Rocca (University of Modena and Reggio, Italy).


    The purpose of this introductory short course is to present Bayesian inferential methods with applications to problems arising in econometrics. Participants in the course will be exposed to the relevant Bayesian theory and computational techniques, including hierarchical Bayesian methods, model comparisons via marginal likelihoods and Bayes factors, and Markov chain Monte Carlo methods. Discussion will center on state space models, multiple change point models, instrumental variable models, and models for longitudinal and clustered data. The methods and ideas will be applied in the estimation of arbitrage-free models of the yield curve. All the techniques will be illustrated with real data and, where possible, instructions on coding the techniques in Winbugs and R will be provided.

    The school will make use of lectures, practical sessions, software demonstrations, informal discussion sessions and presentations of research projects by school participants.


    The school will be held in the conference room of the Pavia department of IMATI CNR (Via Ferrata 1).

    The school will start on Monday, September, 3rd, at 14.30 and it will end on Friday, September, 7th, at 13.00. Welcome cocktail and farewell dinner are planned on September, 3rd and 6th, respectively. Participants will have a free afternoon on Wednesday, September, 5th.


    The registration fees are:

  • EUR 230 for students and postdocs
  • EUR 375 for people from academic and non-profit organisations
  • EUR 750 for all the others
  • including teaching materials, two lunches, welcome cocktail and social dinner. A 50 euros late registration fee will apply after June, 30th ,2007.

    Participants interested in a bed and breakfast, single room accommodation for 4 nights (arrival 3/9 and departure 7/9) at the nearby Collegio Volta should pay an extra 200 euros (only 20 single rooms are available in this student dorm).


  • lecturer
  • school directors and organising committee
  • participants
  • programme
  • suggested prerequisites
  • reading
  • software
  • registration, deadlines and address for inquiries
  • registration form
  • accommodations
  • tourist information
  • travel links and maps
  • how to get to Pavia and CNR-IMATI (Pavia)
  • ABS04 Summer School on Statistics & Gene Expression Genomics: methods and computations
  • ABS05 Summer School on Bayesian Approaches to Evidence Synthesis and Decision Modelling in Health Care
  • ABS06 Summer School on Hierarchical Modelling Approaches for Spatial Data in Environmental and Health Sciences