Likelihood Inflating Sampling Algorithm

05/06/2016
by   Reihaneh Entezari, et al.
0

Markov Chain Monte Carlo (MCMC) sampling from a posterior distribution corresponding to a massive data set can be computationally prohibitive since producing one sample requires a number of operations that is linear in the data size. In this paper, we introduce a new communication-free parallel method, the Likelihood Inflating Sampling Algorithm (LISA), that significantly reduces computational costs by randomly splitting the dataset 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 both simulated and real data.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/19/2013

Asymptotically Exact, Embarrassingly Parallel MCMC

Communication costs, resulting from synchronization requirements during ...
research
07/16/2021

Efficient Bayesian Sampling Using Normalizing Flows to Assist Markov Chain Monte Carlo Methods

Normalizing flows can generate complex target distributions and thus sho...
research
06/01/2020

Distributed Bayesian Varying Coefficient Modeling Using a Gaussian Process Prior

Varying coefficient models (VCMs) are widely used for estimating nonline...
research
11/01/2022

Bayesian Parameter Inference for Partially Observed SDEs driven by Fractional Brownian Motion

In this paper we consider Bayesian parameter inference for partially obs...
research
04/01/2018

Bayesian Mosaic: Parallelizable Composite Posterior

This paper proposes Bayesian mosaic, a parallelizable composite posterio...
research
06/10/2015

Parallelizing MCMC with Random Partition Trees

The modern scale of data has brought new challenges to Bayesian inferenc...
research
08/10/2017

Communication-Free Parallel Supervised Topic Models

Embarrassingly (communication-free) parallel Markov chain Monte Carlo (M...

Please sign up or login with your details

Forgot password? Click here to reset