Accelerating delayed-acceptance Markov chain Monte Carlo algorithms

Delayed-acceptance Markov chain Monte Carlo (DA-MCMC) samples from a probability distribution via a two-stages version of the Metropolis-Hastings algorithm, by combining the target distribution with a "surrogate" (i.e. an approximate and computationally cheaper version) of said distributio...

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Bibliographic Details
Published in:arXiv.org 2019-05
Main Authors: Wiqvist, Samuel, Picchini, Umberto, Julie Lyng Forman, Lindorff-Larsen, Kresten, Boomsma, Wouter
Format: Article
Language:English
Subjects:
Acceleration
Algorithms
Approximation
Bayesian analysis
Computer simulation
Differential equations
Folding
Gaussian process
Markov analysis
Markov chains
Monte Carlo simulation
Protein folding
Proteins
Sampling
Statistical inference
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Summary:Delayed-acceptance Markov chain Monte Carlo (DA-MCMC) samples from a probability distribution via a two-stages version of the Metropolis-Hastings algorithm, by combining the target distribution with a "surrogate" (i.e. an approximate and computationally cheaper version) of said distribution. DA-MCMC accelerates MCMC sampling in complex applications, while still targeting the exact distribution. We design a computationally faster, albeit approximate, DA-MCMC algorithm. We consider parameter inference in a Bayesian setting where a surrogate likelihood function is introduced in the delayed-acceptance scheme. When the evaluation of the likelihood function is computationally intensive, our scheme produces a 2-4 times speed-up, compared to standard DA-MCMC. However, the acceleration is highly problem dependent. Inference results for the standard delayed-acceptance algorithm and our approximated version are similar, indicating that our algorithm can return reliable Bayesian inference. As a computationally intensive case study, we introduce a novel stochastic differential equation model for protein folding data.
ISSN:2331-8422