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2006•Illinois Journal of MathematicsOpen access

Examples comparing importance sampling and the Metropolis algorithm

Federico Bassetti, Persi Diaconis

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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.

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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.

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Available 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.

Key concepts: Metropolis–Hastings algorithm, Markov chain Monte Carlo, Rejection sampling, Markov chain, Sampling (signal processing), Slice sampling, Mathematics, Algorithm

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