Parallel local approximation MCMC for expensive models

25 Dec 2017 Conrad Patrick Davis Andrew Marzouk Youssef Pillai Natesh Smith Aaron

Performing Bayesian inference via Markov chain Monte Carlo (MCMC) can be exceedingly expensive when posterior evaluations invoke the evaluation of a computationally expensive model, such as a system of partial differential equations. In recent work [Conrad et al. JASA 2016, arXiv:1402.1694], we described a framework for constructing and refining local approximations of such models during an MCMC simulation... (read more)

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