Scalable Importance Tempering and Bayesian Variable Selection

1 May 2018Giacomo ZanellaGareth Roberts

We propose a Monte Carlo algorithm to sample from high dimensional probability distributions that combines Markov chain Monte Carlo and importance sampling. We provide a careful theoretical analysis, including guarantees on robustness to high dimensionality, explicit comparison with standard Markov chain Monte Carlo methods and illustrations of the potential improvements in efficiency... (read more)

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