A modified conditional Metropolis–Hastings sampler

Alicia A. Johnson and James M. Flegal

Computational Statistics & Data Analysis, 2014, vol. 78, issue C, 141-152

Abstract: A modified conditional Metropolis–Hastings sampler for general state spaces is introduced. Under specified conditions, this modification can lead to substantial gains in statistical efficiency while maintaining the overall quality of convergence. Results are illustrated in two settings: a toy bivariate Normal model and a Bayesian version of the random effects model.

Keywords: Markov chain Monte Carlo; Metropolis–Hastings; Gibbs sampler; Geometric ergodicity (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:78:y:2014:i:c:p:141-152

DOI: 10.1016/j.csda.2014.04.009

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