Abstract
Importance sampling, particularly sequential and adaptive importance sampling, have emerged as competitive simulation techniques to Markov-chain Monte-Carlo techniques. We compare importance sampling and the Metropolis algorithm as two ways of changing the output of a Markov chain to get a different stationary distribution.
Citation
Federico Bassetti. Persi Diaconis. "Examples comparing importance sampling and the Metropolis algorithm." Illinois J. Math. 50 (1-4) 67 - 91, 2006. https://doi.org/10.1215/ijm/1258059470
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