Local Derivative-Free Approximation of Computationally Expensive Posterior Densities.

ABSTRACT: Bayesian inference using Markov chain Monte Carlo (MCMC) is computationally prohibitive when the posterior density of interest, π, is computationally expensive to evaluate. We develop a derivative-free algorithm GRIMA to accurately approximate π by interpolation over its high-probability density (HPD) region, which is initially unknown. Our local approach reduces the waste of computational budget on approximation of π in the low-probability region, which is inherent in global experimental designs. However, estimation of the HPD region is nontrivial when derivatives of π are not available or are not informative about the shape of the HPD region. Without relying on derivatives, GRIMA iterates (a) sequential knot selection over the estimated HPD region of π

SUBMITTER: Bliznyuk N 

PROVIDER: S-EPMC5978778 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature