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.