RMCMC: A system for updating Bayesian models

F. Din-Houn Lau and Axel Gandy

Computational Statistics & Data Analysis, 2014, vol. 80, issue C, 99-110

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 are 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. The correctness of the estimates and their accuracy are shown in a simulation using a linear Gaussian model.

Keywords: Importance sampling; Markov chain Monte Carlo methods; Monte Carlo techniques; Streaming data; Sports modelling (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:80:y:2014:i:c:p:99-110

DOI: 10.1016/j.csda.2014.06.010

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