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Established in 2020, POSTECH Mathematical Institute for Data Science (MINDS) is the community of researchers in the areas of fundamental data science, machine learning, artificial intelligence, scientific computing, and humanitarian data science. MINDS mission is to provide a platform for collaboration among researchers and to provide various opportunities for students in data science. MINDS also aims to use our data science research to serve our local and global communities pursuing humanitarian data science.

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MINDS SEMINAR

MINDS Seminar Series | Gyuhyeong Goh (Kansas State University) - Scalable Bayesian inference for high-dimensional regression

MINDS SEMINAR
period : 2022-10-25 ~ 2022-10-25
time : 10:00:00 ~ 11:00:00
개최 장소 : Online streaming (Zoom)
Topic : Scalable Bayesian inference for high-dimensional regression
개요
Date 2022-10-25 ~ 2022-10-25 Time 10:00:00 ~ 11:00:00
Speaker Gyuhyeong Goh Affiliation Kansas State University
Place Online streaming (Zoom) Streaming link ID : 688 896 1076 / PW : 54321
Topic Scalable Bayesian inference for high-dimensional regression
Contents The growing influence of high-dimensional regression modeling has led to many remarkable advances in Bayesian variable selection and shrinkage estimation. Due to the computational convenience and theoretical relevance, the use of Gaussian scale mixture priors has become standard practice in high-dimensional Bayesian regression settings. The conditional conjugacy of Gaussian scale mixtures enables us to perform posterior inference via Gibbs sampling. However, when the number of regression coefficients is very large, the computational cost of Gibbs sampling becomes prohibitively expensive as the posterior sampling step requires iterative computations of a large inverse matrix. To address such scalability issue, we propose a scalable Bayesian inference procedure using a new representation of Gaussian scale mixture distributions. The greatest merit of the proposed method is that fast posterior sampling is possible via a partially collapsed Gibbs sampling scheme, which does not require the iterative inverse matrix computation. As an illustration, we show some results from simulation studies and real data analysis.
MinDS MinDS · 2022-10-18 09:08 · Views 468

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