Several papers have appeared recently discussing the issue of how to tune the number of particles used in the particle filter within a particle MCMC algorithm such as particle marginal Metropolis Hastings (PMMH). Three such papers are: Doucet, Arnaud, Michael Pitt, and Robert Kohn. Efficient implementation of Markov chain Monte Carlo when using an unbiased […]
Several papers have appeared recently discussing the issue of how to tune the number of particles used in the particle filter within a particle MCMC algorithm such as particle marginal Metropolis Hastings (PMMH). Three such papers are: Doucet, Arnaud, Michael Pitt, and Robert Kohn. Efficient implementation of Markov chain Monte Carlo when using an unbiased […]
Introduction Particle MCMC (the use of approximate SMC proposals within exact MCMC algorithms) is arguably one of the most important developments in computational Bayesian inference of the 21st Century. The key concepts underlying these methods are described in a famously impenetrable “read paper” by Andrieu et al (2010). Probably the most generally useful method outlined […]
Introduction Particle MCMC (the use of approximate SMC proposals within exact MCMC algorithms) is arguably one of the most important developments in computational Bayesian inference of the 21st Century. The key concepts underlying these methods are described in a famously impenetrable “read paper” by Andrieu et al (2010). Probably the most generally useful method outlined […]
Introduction In a previous post I showed how to construct a PMMH pMCMC algorithm for parameter estimation with partially observed Markov processes. The inner loop of a pMCMC algorithm consists of running a particle filter to construct an unbiased estimate of marginal likelihood. This inner loop is the place where the code spends almost all […]
In the previous post I gave a quick introduction to the CRAN R package smfsb, and how it can be used for simulation of Markov processes determined by stochastic kinetic networks. In this post I’ll show how to use data and particle MCMC techniques in order to carry out Bayesian inference for the parameters of [...]
