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SDS Seminar Series: Yanxin Li (Friday 9/25)

The Fall 2020 SDS Seminar Series continues with Yanxin Li (PhD candidate; The University of Texas at Austin, Department of Statistics and Data Sciences), on Friday, September 25th, 2020 from 2:00 p.m. to 3:00 p.m. via Zoom. Please contact stat.admin@austin.utexas.edu for the Zoom link.

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Title: "Latent Slice Sampling"

Abstract: In Bayesian inference, the two most popular sampling methods for constructing suitable transition kernels are the Metropolis-Hastings method and the Gibbs sampler. However, the Metropolis-Hastings method requires a good proposal distribution, otherwise the chain might become trapped in local mode or simply not move at all. To address this “sticking” problem, a Markov Chain Monte Carlo (MCMC) sampling method called latent slice sampling is proposed, to apply in a vast range of research paradigms where the Metropolis-Hastings algorithm is currently used. It is an alternative method which can replace Metropolis-Hastings method for faster convergence, better mixing of the chain, no tuning and no accept/reject component. The latent slice sampler is applied to both the discrete and continuous random variables. This talk will be an extension of my candidacy talk and mainly focuses on multivariate latent slice sampler, adaptive latent slice sampling on continuous variables, as well as theories behind the sampling algorithm.

SDS Seminar Series: Dr. Magdalena Bennett
SDS Seminar Series: Xinjie Fan (Friday 9/4)

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Monday, 28 September 2020

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