A review of multiple try MCMC algorithms for signal processing

作者：Martino Luca^*

来源：Digital Signal Processing, 2018, 75: 134-152.

DOI：10.1016/j.dsp.2018.01.004

• Markov Chain Monte Carlo
• Multiple Try Metropolis
• Particle Metropolis-Hastings
• Particle filtering
• Monte Carlo methods
• Bayesian inference

Many applications in signal processing require the estimation of some parameters of interest given a set of observed data. More specifically, Bayesian inference needs the computation of a-posteriori estimators which are often expressed as complicated multi-dimensional integrals. Unfortunately, analytical expressions for these estimators cannot be found in most real-world applications, and Monte Carlo methods are the only feasible approach. A very powerful class of Monte Carlo techniques is formed by the Markov Chain Monte Carlo (MCMC) algorithms. They generate a Markov chain such that its stationary distribution coincides with the target posterior density. In this work, we perform a thorough review of MCMC methods using multiple candidates in order to select the next state of the chain, at each iteration. With respect to the classical Metropolis-Hastings method, the use of multiple try techniques foster the exploration of the sample space. We present different Multiple Try Metropolis schemes, Ensemble MCMC methods, Particle Metropolis-Hastings algorithms and the Delayed Rejection Metropolis technique. We highlight limitations, benefits, connections and differences among the different methods, and compare them by numerical simulations.

• 出版日期2018-4