# Posterior Approximation by Interpolation for Bayesian Inference in Computationally Expensive Statistical Models

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## Abstract

Markov Chain Monte Carlo (MCMC) is nowadays a standard
approach to numerical computation of integrals of the
posterior density

In Chapter 1,

In Chapter 2, we relax the assumptions about

In Chapter 3, we study statistical models where it is
possible to identify a minimal subvector

Our experiments indicate that our methods produce results similar to those when the true expensive posterior density is sampled by MCMC while reducing computational costs by well over an order of magnitude.