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Probabilistic factorizations of big tables and networks
March 17 @ 11:00 am
It is common to collect high-dimensional data that are structured as a multiway array or tensor; examples include multivariate categorical data that are organized as a contingency table, sequential data on nucleotides or animal vocalizations, and neuroscience data on brain networks. In each of these cases, there is interest in doing inference on the joint probability distribution of the data and on interpretable functionals of this probability distribution. The goal is to avoid restrictive parametric assumptions, enable both statistical and computational scaling to high dimensional low sample size cases, and maintain a (hopefully accurate) characterization of uncertainty. In this talk, the focus is on probabilistic factorizations and Bayesian inference algorithms relying on Markov chain Monte Carlo (MCMC) sampling. Novel classes of factorizations are proposed, practical and theoretical properties are discussed, scalable algorithms are developed, and a variety of applications are considered.