ABS13 - 2013 Applied Bayesian Statistics School BAYESIAN METHODS FOR VARIABLE SELECTION WITH APPLICATIONS TO HIGH-DIMENSIONAL DATA Villa del Grumello, Como, Italy June, 17-21, 2013

Last update: 11/06/2013

The Applied Bayesian Statistics summer school has been running since 2004. From 2012 it is organized by

We acknowledge the contribution by Centro di Cultura Scientifica "Alessandro Volta" and Camera di Commercio di Como.

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, Spatial Data in Environmental and Health Sciences, Bayesian Methods and Econometrics, Bayesian Decision Problems in Biostatistics and Clinical Trials, Bayesian Methodology for Clustering, Classification and Categorical Data Analysis, Bayesian Machine Learning with Biomedical Applications, Hierarchical Modeling for Environmental Processes and Stochastic Modelling for Systems Biology.

The topic chosen for the 2013 school is Bayesian Methods for Variable Selection with Applications to High-dimensional Data.

The lecturer will be Marina Vannucci, Department of Statistics, Rice University, Houston, USA

She will be assisted by Raffaele Argiento (CNR IMATI, Italy).

This course will cover Bayesian methods for variable selection and applications. Various modeling settings will be considered, starting with the widely used linear regression models. Bayesian methods for variable selection have been successfully employed in linear setting models, making problems with hundreds of regressor variables and a few samples quite feasible. These methods use mixing priors on the regression coefficients to do the selection and fast Markov Chain Monte Carlo stochastic search approaches to sample from posterior distributions. Extensions of the methodologies to other linear settings will also be considered, in particular to handle categorical responses, via probit models, and survival data, via accelerated failure time models. Applications of the methodologies will focus on high-dimensional data from genomic studies that use high-throughtput expression levels of thausands of genes. For such applications, models and inferential algorithms will be modified to incorporate specific information, such as data substructure and biological knowledge on gene functions. The last part of the course will address variable selection for a different modeling setting, that is mixture models, both unsupervised (for sample clustering) and supervised (for discriminant analysis). In mixture models variable selection is achieved via latent binary vectors that identify the discriminating variables and are updated via a Metropolis algorithm. In the clustering setting, inference on the sample allocations is obtained either via reversible jump MCMC or split-merge MCMC techniques. Performances of the methodologies will be illustrated on simulated data and on DNA microarray data. The course will end with a brief description of additional topics, such as the use of variable selection priors in nonlinear settings, via Gaussian processes, and for the analysis of functional data.

The school will make use of lectures, practical sessions, software demonstrations, informal discussion sessions and presentations of research projects by school participants. The slides and background reading material will be distributed to the students before the start of the course.

The prerequisites for this course will include basic knowledge of algebra and calculus, probability, statistical modelling, and data analysis. Some background on Bayesian analysis and the basics of MCMC is desiderable. This course will be beneficial to graduate students, post-docs and researchers both from academia, government, and industry whose area of activity is Statistics, Modelling, Bioinformatics, Biostatistics, Genomics and Systems Biology.

There is no required course text but we recommend the books "A first course in Bayesian statistical methods" (2009), by Peter Hoff, Springer Verlag and "Bayesian Data Analysis" (2004), by Andrew Gelman, John B. Carlin, Hal S. Stern and Donald B. Rubin, 2nd Ed., Chapman & Hall/CRC, as background reading (for participants not familiar with Bayesian statistics) before the course. Some articles will be provided by the lecturer just before the course.

Participants are expected to bring their own laptop with recent versions of R and WinBUGS installed, in order to actively participate in the practical sessions.

All information about R can be found on the R Project Website.

The following R packages should also be installed

The 2013 school will be held at Villa del Grumello, a magnificent villa located in the city of Como, along the Lake Como shoreline.

Please note that the number of available places is limited.

The school will start on Monday, June, 17th, at 14.00 and it will end on Friday, June, 21st, at 13.00. Welcome buffet and farewell dinner are planned on June, 17th and 20th, respectively. Participants will have a free afternoon on Wednesday, June, 19th.

The registration fees (for payments before April, 15th, 2013) are:

Meals, snacks and drinks are available nearby and they are the participants' responsibility.