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DTSTAMP:20260728T231731Z
DESCRIPTION:Shuie Li  Recursive Polya tree mixture model: a new Bayesian no
 nparametric model.  Abstract:   Description: We develop a new Bayesian non
 parametric model called recursive Polya tree mixture model (RPTMM). This m
 odel is very simple and does not require any analytical computation\, but 
 can approximate other complicated Bayesian nonparametric approaches.  In a
 ddition\, it is very easy for our model to handle some difficult problems 
 in classical Bayesian nonparametric models. In this talk\, we will discuss
  a baseball player’s data (Brown\, 2008)\, and a rolling thumbtacks data t
 hat was originally analyzed through sequential Monte Carlo method (Liu\, 1
 996). We will also compare our RPTMM to a meta-analysis data with a compli
 cated conditional Dirichlet process (Burr and Doss\, 2005)\, and develop a
  Bayesian semiparametric AFT model (Hanson and Johnson\, 2004). This talk 
 is based on the joint work with professors David Stephens and James Hanley
 .  Bio:   Li Shujie is a PhD (Biostatistics) student at McGill University\
 , working under the supervision of Drs. James Hanley and David Stephens. H
 is research interest is to develop flexible Bayesian nonparametric and sem
 i-parametric models for medical applications\, including random-effects me
 ta-analysis\, Bayesian semi-parametric accelerated failure time model for 
 survival data\, and recurrent data analysis.   
DTSTART:20130122T210000Z
DTEND:20130122T220000Z
LOCATION:Purvis Hall\, CA\, QC\, Montreal\, H3A 1A2\, 1020 avenue des Pins 
 Ouest
SUMMARY:Biostatistics Seminars Winter 2013
URL:https://www.mcgill.ca/epi-biostat-occh/channels/event/biostatistics-sem
 inars-winter-2013-219814
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