Edge-Selection Priors for Graphical Models and Applications to Complex Biological Data
Marina Vannucci, Rice University
Friday, December 6, 2019 - 3:30pm
There is now a huge literature on Bayesian methods for variable selection in linear models that use spike-and-slab priors.
Such methods, in particular, have been quite successful for applications in a variety of different fields. A parallel methodological development has happened in graphical models, where priors are specified on precision matrices. In this talk I will describe priors for edge selection for the estimation of multiple graphs that may share common features, such as presence/absence of edges or strengths of connections. I will also describe modeling frameworks for non-Gaussian data and discuss computational challenges.
I will motivate the development of the models using specific applications from neuroimaging and from studies that use biomedical data.
Short bio: Dr. Vannucci is Noah Harding Professor of Statistics at Rice University and holds an Adjunct appointment with the Department of Biostatistics at the UT MD Anderson Cancer Center. She received a Laurea (B.S.) degree in Mathematics and a Ph.D. degree in Statistics, both from the University of Florence, Italy. Prior to joining Rice, she held positions at the University of Kent at Canterbury, UK and in the Department of Statistics at Texas A&M University, TX. She has been at Rice since 2007, and served as Department Chair during 2014-2019.
Dr. Vannucci is an elected Member of the ISI and an elected Fellow of the ASA, IMS, AAAS and ISBA. She was the 2018 President of ISBA and the Editor-in-Chief of Bayesian Analysis, the flagship journal of ISBA, in 2013-2015.
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