Posterior-based proposals for speeding up Markov chain Monte Carlo

Posterior-based proposals for speeding up Markov chain Monte Carlo

Markov chain Monte Carlo (MCMC) is widely used for Bayesian inference in models of complex systems. Performance, however, is often unsatisfactory in models with many latent variables due to so-called poor mixing, necessitating the development of application-specific implementations. This paper intro...

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Bibliographic Details
Main Authors: C. M. Pooley, S. C. Bishop, A. Doeschl-Wilson, G. Marion
Format: Article
Language:English
Published: The Royal Society 2019-11-01
Series:Royal Society Open Science
Subjects:
posterior-based proposal
markov chain monte carlo
bayesian inference
mixed model
stochastic volatility
statistical genetics
Online Access:https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.190619

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https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.190619

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