Efficient Interpolation of Computationally Expensive Posterior Densities With Variable Parameter Costs.

ABSTRACT: Markov chain Monte Carlo (MCMC) is nowadays a standard approach to numerical computation of integrals of the posterior density π of the parameter vector η. Unfortunately, Bayesian inference using MCMC is computationally intractable when the posterior density π is expensive to evaluate. In many such problems, it is possible to identify a minimal subvector β of η responsible for the expensive computation in the evaluation of π. We propose two approaches, DOSKA and INDA, that approximate π by interpolation in ways that exploit this computational structure to mitigate the curse of dimensionality. DOSKA interpolates π directly while INDA interpolates π indirectly by interpolating functions, for example, a regression function, upon w

SUBMITTER: Bliznyuk N 

PROVIDER: S-EPMC5978746 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature