Title	Recent Developments of Iterative Monte Carlo Methods for Big Data Analysis (Faming Liang & Chuanhai Liu)
Creator	Liang, Faming
Publisher	Banff International Research Station for Mathematical Innovation and Discovery
Date Issued	2014-02-11
Description	Iterative Monte Carlo methods, such as MCMC, stochastic approximation, and EM, have proven to be very powerful tools for statistical data analysis. However, their computer- intensive nature, which typically require a large number of iterations and a complete scan of the full dataset for each iteration, precludes their use for big data analysis. We will provide an overview of the recent developments of iterative Monte Carlo methods for big data analysis. The portion by Liang will focus on the developments of the MCMC and stochastic approximation methods, and that by Liu will focus on the developments of the EM method.
Extent	107 minutes
Subject	Mathematics; Statistics; Biology and other natural sciences; Applied 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	2015-02-02
Provider	Vancouver : University of British Columbia Library
Rights	Attribution-NonCommercial-NoDerivs 2.5 Canada
DOI	10.14288/1.0044474
URI	http://hdl.handle.net/2429/52006
Affiliation	Non UBC
Peer Review Status	Unreviewed
Scholarly Level	Faculty
Rights URI	http://creativecommons.org/licenses/by-nc-nd/2.5/ca/
Aggregated Source Repository	DSpace

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