Assessing the Convergence of Markov Chain Monte Carlo Methods for Bayesian Inference of Phylogenetic Trees In biology, it is commonly of interest to investigate the ancestral pattern that gave rise to a currently existing group of individuals, such as genes or species. This ancestral pattern is frequently represented pictorially by a phylogenetic tree. Due to the growing popularity of Bayesian ...

Motivated by the success of genetic algorithms and simulated annealing in hard optimization problems, the authors propose a new Markov chain Monte Carlo (MCMC) algorithm called an evolutionary Monte Carlo algorithm. This algorithm has incorporated several attractive features of genetic algorithms and simulated annealing into the framework of MCMC. It works by simulating a population of Markov c...

In recent years, parallel processing has become widely available to researchers. It can be applied in an obvious way in the context of Monte Carlo simulation, but techniques for “parallelizing” Markov chain Monte Carlo (MCMC) algorithms are not so obvious, apart from the natural approach of generating multiple chains in parallel. While generation of parallel chains is generally the easiest appr...

Hidden Markov models have proved to be a very exible class of models, with many and diverse applications. Recently Markov chain Monte Carlo (MCMC) techniques have provided powerful computational tools to make inferences about the parameters of hidden Markov models, and about the unobserved Markov chain, when the chain is deened in discrete time. We present a general algorithm, based on reversib...

Sequential Monte Carlo (SMC) methods are not only a popular tool in the analysis of state–space models, but offer an alternative to Markov chain Monte Carlo (MCMC) in situations where Bayesian inference must proceed via simulation. This paper introduces a new SMC method that uses adaptive MCMC kernels for particle dynamics. The proposed algorithm features an online stochastic optimization proce...

The MCMC (Markov Chain Monte-Carlo) method [1] has played an important role in study of complex systems with many degrees of freedom. For example, MCMC has been applied to various many-body problems such as proteins [2], spin systems [3], and lattice gauge theory [4]. Although the method has achieved great success, there are systems where Monte-Carlo sampling does not work due to local minima o...

In this paper we use Markov chain Monte Carlo (MCMC) methods in order to estimate and compare GARCH models from a Bayesian perspective. We allow for possibly heavy tailed and asymmetric distributions in the error term. We use a general method proposed in the literature to introduce skewness into a continuous unimodal and symmetric distribution. For each model we compute an approximation to the ...

Introduction Bayesian hierarchical models with random effects are one of the most widely used methods in modern disease mapping, as a superior alternative to standardized ratios. These models are traditionally fitted through Markov Chain Monte Carlo sampling (MCMC). Due to the nature of the hierarchical models and random effects, the convergence of MCMC is very slow and unpredictable. Recently,...

We tackle the problem of object recognition using a Bayesian approach. A marked point process [1] is used as a prior model for the (unknown number of) objects. A sample is generated via Markov chain Monte Carlo (MCMC) techniques using a novel combination of Metropolis-coupled MCMC (MCMCMC) [2] and the Delayed Rejection Algorithm (DRA) [4]. The method is illustrated on some synthetic data contai...