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Initializing adaptive importance sampling with Markov chains
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Adaptive importance sampling is a powerful tool to sample from complicated target densities, but its success depends sensitively on the initial proposal density. An algorithm is presented to automatically perform the initialization using Markov chains and hierarchical clustering. The performance is checked on challenging multimodal examples in up to 20 dimensions and compared to results from nested sampling. Our approach yields a proposal that leads to rapid convergence and accurate estimation of overall normalization and marginal distributions.
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Cited by 1 Pith paper
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Importance Nested Sampling and the MultiNest Algorithm
Importance nested sampling re-uses all MultiNest points, including those previously discarded, as a pseudo-importance sample to estimate Bayesian evidence with substantially higher accuracy than vanilla nested sampling.
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