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Acceleration of the Multiple-Try Metropolis algorithm using antithetic and stratified sampling


The Multiple-Try Metropolis is a recent extension of the Metropolis algorithm in which the next state of the chain is selected among a set of proposals. We propose a modification of the Multiple-Try Metropolis algorithm which allows for the use of correlated proposals, particularly antithetic and stratified proposals. The method is particularly useful for random walk Metropolis in high dimensional spaces and can be used easily when the proposal distribution is Gaussian. We explore the use of quasi Monte Carlo (QMC) methods to generate highly stratified samples. A series of examples is presented to evaluate the potential of the method.

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Correspondence to Radu V. Craiu.

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Craiu, R.V., Lemieux, C. Acceleration of the Multiple-Try Metropolis algorithm using antithetic and stratified sampling. Stat Comput 17, 109 (2007). https://doi.org/10.1007/s11222-006-9009-4

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  • Antithetic variates
  • Markov Chain Monte Carlo
  • Extreme antithesis
  • Korobov rule
  • Latin Hypercube sampling
  • Quasi Monte Carlo
  • Sobol’ sequence
  • Multiple-Try Metropolis
  • Random-Ray Monte Carlo