Illinois Journal of Mathematics

Examples comparing importance sampling and the Metropolis algorithm

Federico Bassetti and Persi Diaconis

Full-text: Open access

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.

Article information

Source
Illinois J. Math., Volume 50, Number 1-4 (2006), 67-91.

Dates
First available in Project Euclid: 12 November 2009

Permanent link to this document
https://projecteuclid.org/euclid.ijm/1258059470

Digital Object Identifier
doi:10.1215/ijm/1258059470

Mathematical Reviews number (MathSciNet)
MR2247824

Zentralblatt MATH identifier
1102.60060

Subjects
Primary: 60J10: Markov chains (discrete-time Markov processes on discrete state spaces)
Secondary: 65C05: Monte Carlo methods

Citation

Bassetti, Federico; Diaconis, Persi. Examples comparing importance sampling and the Metropolis algorithm. Illinois J. Math. 50 (2006), no. 1-4, 67--91. doi:10.1215/ijm/1258059470. https://projecteuclid.org/euclid.ijm/1258059470


Export citation