Accelerating Asymptotically Exact MCMC for Computationally Intensive Models via Local Approximations

Volume: 111, Issue: 516, Pages: 1591 - 1607
Published: Oct 1, 2016
Abstract
We construct a new framework for accelerating Markov chain Monte Carlo in posterior sampling problems where standard methods are limited by the computational cost of the likelihood, or of numerical models embedded therein. Our approach introduces local approximations of these models into the Metropolis–Hastings kernel, borrowing ideas from deterministic approximation theory, optimization, and experimental design. Previous efforts at integrating...
Paper Details
Title
Accelerating Asymptotically Exact MCMC for Computationally Intensive Models via Local Approximations
Published Date
Oct 1, 2016
Volume
111
Issue
516
Pages
1591 - 1607
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