Likelihood Inflated Sampling Algorithm for Bayesian Regression Trees with Large Data

Markov Chain Monte Carlo (MCMC) sampling from a posterior distribution corresponding to a massive data set can be computationally prohibitive as producing one sample requires a number of operations that is linear in the data size. A new communication-free parallel method, the “Likelihood Inflating Sampling Algorithm (LISA),” is introduced. LISA significantly reduces computational costs by randomly splitting the data set into smaller subsets and running MCMC methods independently in parallel on each subset using different processors. Each processor will be used to run an MCMC chain that samples sub-posterior distributions which are defined using an “inflated” likelihood function. We develop a strategy for combining the draws from different sub-posteriors to study the full posterior of the Bayesian Additive Regression Trees (BART) model. The performance of the method is tested using simulated data and a large socio-economic study.

Date and Time: 

Monday, June 4, 2018 - 14:30 to 15:00

Co-authors (not including you): 

Reihaneh Entezari
University of Toronto
Jeffrey Rosenthal
University of Toronto

Language of Oral Presentation: 

English

Language of Visual Aids: 

English

Type of Presentation: 

Invited

Session: 

Speaker

First Name Middle Name Last Name Primary Affiliation
Virgil Radu Craiu University of Toronto