Title	Some applications of MCMC perturbations with high-dimensional shrinkage priors
Creator	Bhattacharya, Anirban
Publisher	Banff International Research Station for Mathematical Innovation and Discovery
Date Issued	2019-04-10T09:05
Description	Markov chain Monte Carlo (MCMC) algorithms are commonly used to fit complex hierarchical models to data. In this talk, we shall discuss some recent efforts to scale up Bayesian computation in high-dimensional and shape-constrained regression problems. The common underlying theme is to perturb the transition kernel of an exact MCMC algorithm to ease the computational cost per step while maintaining accuracy. The effects of such approximations are studied theoretically, and new algorithms are developed for the horseshoe prior and constrained Gaussian process priors in various applications.
Extent	41.0 minutes
Subject	Mathematics; Statistics
Type	Moving Image
File Format	video/mp4
Language	eng
Notes	Author affiliation: Texas A&M University
Series	BIRS Workshop Lecture Videos (Banff, Alta)
Date Available	2019-10-08
Provider	Vancouver : University of British Columbia Library
Rights	Attribution-NonCommercial-NoDerivatives 4.0 International
DOI	10.14288/1.0383314
URI	http://hdl.handle.net/2429/71855
Affiliation	Non UBC
Peer Review Status	Unreviewed
Scholarly Level	Researcher
Rights URI	http://creativecommons.org/licenses/by-nc-nd/4.0/
Aggregated Source Repository	DSpace

Open Collections

BIRS Workshop Lecture Videos