On the flexibility of the design of multiple try Metropolis schemes
By Luca Martino, Jesse Read. 2012
64 citations recorded by the source corpus.
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.
Subfields: Computational Theory and Mathematics, Electrical and Electronic Engineering, Statistics and Probability, Probabilistic & Bayesian Methods
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In Computational Theory and Mathematics
In Electrical and Electronic Engineering
In Statistics and Probability
In Probabilistic & Bayesian Methods
Citations in the corpus
- Dynamical Sampling with Langevin Normalization Flows (2019, background): “MALA exploits the Metropolis-Hasting correction [28] to satisfy the detailed balance [42], which ensures that the samples generated from Langevin diffusions will converge to the target distribution.”