Vi ricordiamo che ABS13, decima edizione della scuola estiva ABS
(Applied Bayesian Statistics), si svolgera' a Como, nella prestigiosa
Villa del Grumello, dal 17 al 21 Giugno 2013.
La relatrice sara' la Prof.ssa Marina Vannucci (Rice University,
Houston, USA). Il tema sara' "BAYESIAN METHODS FOR VARIABLE SELECTION
WITH APPLICATIONS TO HIGH-DIMENSIONAL DATA".
Vi segnaliamo che sono ancora disponibili dei posti e che e' possibile
pagare la tassa d'iscrizione ridotta entro il 15 Aprile.
Guido Consonni e Fabrizio Ruggeri
Direttori di ABS13
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Summer school on BAYESIAN METHODS FOR VARIABLE SELECTION WITH
APPLICATIONS TO HIGH-DIMENSIONAL DATA, Como, Italy
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* ABS13 *
*******************************
Applied Bayesian Statistics School
BAYESIAN METHODS FOR VARIABLE SELECTION WITH APPLICATIONS TO
HIGH-DIMENSIONAL DATA
June, 17 - 21, 2013 - Villa del Grumello, Como, Italy
Lecturer
Professor Marina VANNUCCI
Department of Statistics, Rice University, Houston, USA
Programme and registration details are available at
>>>> www.mi.imati.cnr.it/conferences/abs13.html <<<<
Interested people are invited to contact the ABS13 Secretariat at
abs13 at mi.imati.cnr.it
--------------------- COURSE OUTLINE ----------------------------------------
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 thousands
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.
--
Fabrizio Ruggeri fabrizio AT mi.imati.cnr.it
CNR IMATI tel +39 0223699532
Via Bassini 15 fax +39 0223699538
I-20133 Milano (Italy) www.mi.imati.cnr.it/~fabrizio