Markov chain Monte Carlo methods with applications to signal processing
Signal Processing - Special section on Markov Chain Monte Carlo (MCMC) methods for signal processing
Multipoint metropolis method with application to hybrid Monte Carlo
Journal of Computational Physics
Understanding Molecular Simulation: From Algorithms to Applications
Understanding Molecular Simulation: From Algorithms to Applications
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Acceleration of the Multiple-Try Metropolis algorithm using antithetic and stratified sampling
Statistics and Computing
Monte Carlo Strategies in Scientific Computing
Monte Carlo Strategies in Scientific Computing
Interacting multiple try algorithms with different proposal distributions
Statistics and Computing
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The multiple try Metropolis (MTM) method is a generalization of the classical Metropolis---Hastings algorithm in which the next state of the chain is chosen among a set of samples, according to normalized weights. In the literature, several extensions have been proposed. In this work, we show and remark upon the flexibility of the design of MTM-type methods, fulfilling the detailed balance condition. We discuss several possibilities, show different numerical simulations and discuss the implications of the results.