# Rate-optimal refinement strategies for local approximation MCMC

@article{Davis2020RateoptimalRS, title={Rate-optimal refinement strategies for local approximation MCMC}, author={Andrew D. Davis and Youssef M. Marzouk and Aaron Smith and Natesh S. Pillai}, journal={arXiv: Computation}, year={2020} }

Many Bayesian inference problems involve target distributions whose density functions are computationally expensive to evaluate. Replacing the target density with a local approximation based on a small number of carefully chosen density evaluations can significantly reduce the computational expense of Markov chain Monte Carlo (MCMC) sampling. Moreover, continual refinement of the local approximation can guarantee asymptotically exact sampling. We devise a new strategy for balancing the decay… Expand

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