RMCMC: A System for Updating Bayesian Models

TL;DR

The paper introduces RMCMC, a system that efficiently updates Bayesian model estimates using weighted samples and MCMC, ensuring controlled error bounds and applicability to dynamic probabilistic models.

Contribution

It presents a novel system for updating Bayesian estimates with a focus on error control and sample management, integrating MCMC for sample generation.

Findings

System maintains sample quality and estimate accuracy.

Demonstrated effectiveness on football league prediction.

Validated correctness through linear Gaussian model simulation.

Abstract

A system to update estimates from a sequence of probability distributions is presented. The aim of the system is to quickly produce estimates with a user-specified bound on the Monte Carlo error. The estimates are based upon weighted samples stored in a database. The stored samples are maintained such that the accuracy of the estimates and quality of the samples is satisfactory. This maintenance involves varying the number of samples in the database and updating their weights. New samples are generated, when required, by a Markov chain Monte Carlo algorithm. The system is demonstrated using a football league model that is used to predict the end of season table. Correctness of the estimates and their accuracy is shown in a simulation using a linear Gaussian model.