Seminars

Seminar Schedule in Google Calendar
iCal link

Statistics
TBA
Tue 14th October 2014
11:00am
TBA
Show Abstract

Statistics
ESB 4192
Tue 7th October 2014
11:00am
Bayesian Regression Trees, Nonparametric Heteroscedastic Regression Modeling and MCMC Sampling
Show Abstract
Bayesian additive regression trees (BART) have become increasingly popular as flexible and scalable non-parametric models useful in many modern applied 
statistics regression problems. They bring many advantages to the practitioner dealing with large datasets and complex non-linear response surfaces, such as 
the matrix-free formulation and the lack of a requirement to specify a regression basis a priori. However, there are some known challenges to this modeling approach, 
such as poor mixing of the MCMC sampler and inappropriate uncertainty intervals when the assumed homoscedastic variance model is violated.  In this talk, we
introduce a new Bayesian regression tree model that allows for possible heteroscedasticity in the variance model and devise novel MCMC samplers that appear 
to adequately explore the posterior tree space of this model.

a place of mind, The University of British Columbia

Department of Statistics

Department of Statistics, University of British Columbia
3182 Earth Sciences Building
2207 Main Mall
Vancouver, BC, Canada V6T 1Z4
Tel: 604.822.0570
Fax: 604.822.6960

Emergency Procedures | Accessibility | Contact UBC | © Copyright The University of British Columbia