Monday, January 7, 2019

Monday, January 7, 2019 — 4:00 PM EST

Efficient Bayesian Approaches for Big Data and Complex Models

Bayesian inference methods are essential for modern data analysis. Ever growing datasets and model complexities, however, pose the major challenges to classical approaches and have motivated many advancements on scalable inference methods and efficient methods that can handle complicated model structures. In this talk, I will describe some recent work on scalable Bayesian inference methods and efficient learning algorithms for complex models, with applications in machine learning and computational biology. By exploiting the regularity of the underlying probabilistic models, I propose an alternative scalable MCMC approach without sacrificing the exploration efficiency, overcoming a potential drawback of classical stochastic gradient MCMC methods. We extend a state-of-the-art MCMC algorithm, Hamiltonian Monte Carlo, to models with both continuous and discrete (structured) parameters, and successfully apply it to Bayesian phylogenetic inference, an important discipline of evolutionary biology that focuses on the reconstruction of the tree of life. Moreover, I propose a novel graphical model, subsplit Bayesian networks (SBNs), that can provide flexible distributions on phylogenetic trees. The flexibility of SBNs not only allows efficient tree probability estimators, but also enables a general variational framework for Bayesian phylogenetic inference that has promising speed and scalability compared to the current random-walk MCMC approaches.

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