Neural Comput - A Monte Carlo Metropolis-Hastings algorithm for sampling from distributions with intractable normalizing constants.

Tópicos

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Resumo

Simulating from distributions with intractable normalizing constants has been a long-standing problem in machine learning. In this letter, we propose a new algorithm, the Monte Carlo Metropolis-Hastings (MCMH) algorithm, for tackling this problem. The MCMH algorithm is a Monte Carlo version of the Metropolis-Hastings algorithm. It replaces the unknown normalizing constant ratio by a Monte Carlo estimate in simulations, while still converges, as shown in the letter, to the desired target distribution under mild conditions. The MCMH algorithm is illustrated with spatial autologistic models and exponential random graph models. Unlike other auxiliary variable Markov chain Monte Carlo (MCMC) algorithms, such as the M?ller and exchange algorithms, the MCMH algorithm avoids the requirement for perfect sampling, and thus can be applied to many statistical models for which perfect sampling is not available or very expensive. The MCMH algorithm can also be applied to Bayesian inference for random effect models and missing data problems that involve simulations from a distribution with intractable integrals.

Resumo Limpo

simul distribut intract normal constant longstand problem machin learn letter propos new algorithm mont carlo metropolishast mcmh algorithm tackl problem mcmh algorithm mont carlo version metropolishast algorithm replac unknown normal constant ratio mont carlo estim simul still converg shown letter desir target distribut mild condit mcmh algorithm illustr spatial autologist model exponenti random graph model unlik auxiliari variabl markov chain mont carlo mcmc algorithm mller exchang algorithm mcmh algorithm avoid requir perfect sampl thus can appli mani statist model perfect sampl avail expens mcmh algorithm can also appli bayesian infer random effect model miss data problem involv simul distribut intract integr

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