RMCMC: A system for updating Bayesian models
Copyright policy )RMCMC: A system for updating Bayesian models
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.
Statistics and Probability, FOS: Computer and information sciences, Technology, Importance sampling, 330, Statistics & Probability, Markov chain Monte Carlo methods, CHAIN MONTE-CARLO, Applications of statistics, Methodology (stat.ME), DISTRIBUTIONS, Sports modelling, Interdisciplinary Applications, Econometrics, Statistics - Methodology, Science & Technology, SCORES, Computation Theory And Mathematics, Applied Mathematics, Statistics, Monte Carlo methods, streaming data, Monte Carlo techniques, Computational Mathematics, importance sampling, Computational Theory and Mathematics, stat.ME, Streaming data, Physical Sciences, Computer Science, Numerical analysis or methods applied to Markov chains, Computational methods for problems pertaining to statistics, sports modelling, Mathematics
Statistics and Probability, FOS: Computer and information sciences, Technology, Importance sampling, 330, Statistics & Probability, Markov chain Monte Carlo methods, CHAIN MONTE-CARLO, Applications of statistics, Methodology (stat.ME), DISTRIBUTIONS, Sports modelling, Interdisciplinary Applications, Econometrics, Statistics - Methodology, Science & Technology, SCORES, Computation Theory And Mathematics, Applied Mathematics, Statistics, Monte Carlo methods, streaming data, Monte Carlo techniques, Computational Mathematics, importance sampling, Computational Theory and Mathematics, stat.ME, Streaming data, Physical Sciences, Computer Science, Numerical analysis or methods applied to Markov chains, Computational methods for problems pertaining to statistics, sports modelling, Mathematics
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