Abstract: We propose a sequential Markov chain Monte Carlo (SMCMC) algorithm to sample from a sequence of probability distributions, corresponding to posterior distributions at different times in on-line applications. SMCMC proceeds as in usual MCMC but with the stationary distribution updated appropriately each time new data arrive. SMCMC has advantages over sequential Monte Carlo (SMC) in avo...

We consider the design of Markov chain Monte Carlo (MCMC) methods for large-scale, distributed, heterogeneous compute facilities, with a focus on synthesising sample sets across multiple runs performed in parallel. While theory suggests that many independent Markov chains may be run and their samples pooled, the well-known practical problem of quasi-ergodicity, or poor mixing, frustrates this o...

Markov chain Monte Carlo (MCMC) is a popular class of algorithms to sample from a complex distribution. A key issue in the design of MCMC algorithms is to improve the proposal mechanism and the mixing behaviour. This has led some authors to propose the use of a population of MCMC chains, while others go even further by integrating techniques from evolutionary computation (EC) into the MCMC fram...

MCL is an extremely general framework for localization that can be used with almost any sort of sensor and map. Its power comes mainly from two aspects: • The use of a probability model that reformulates the problem of global localization as a tractable local conditional probability. This allows position information to be gleaned from sensor inputs that at any given time provide only very vague...

Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods have emerged as the two main tools to sample from high-dimensional probability distributions. Although asymptotic convergence of MCMC algorithms is ensured under weak assumptions, the performance of these latters is unreliable when the proposal distributions used to explore the space are poorly chosen and/or if highly corr...

abstract background: keratoconus is a bilateral corneal disease, which one way to cure it is to transplant. the transplantation may be rejected by recipient's immune system, which leads to failure of the graft. this study aimed to analysis the factors affecting bilateral corneal graft rejection based on copula function. methods: a sample of bilateral graft rejection times was assessed. since co...

New methodology for fully Bayesian mixture analysis is developed, making use of reversible jump Markov chain Monte Carlo methods that are capable of jumping between the parameter subspaces corresponding to different numbers of components in the mixture. A sample from the full joint distribution of all unknown variables is thereby generated, and this can be used as a basis for a thorough present...

Markov chain Monte Carlo and sequential Monte Carlo methods have emerged as the two main tools to sample from high dimensional probability distributions.Although asymptotic convergence of Markov chain Monte Carlo algorithms is ensured under weak assumptions, the performance of these algorithms is unreliable when the proposal distributions that are used to explore the space are poorly chosen and...