In an effort to extend the tempering methodology, we propose simulated sintering as a general framework for designing Markov chain Monte Carlo algorithms. To implement sintering, one identifies a family of probability distributions, all related to the target one and defined on spaces of different dimensions. Then, a Markov chain is constructed to move across these spaces, with the hope that the...

The Bayes factor is employed to select covariates for a hierarchical model applied to a collection of hospital admission counts. Integrals representing the Bayes factor numerator and denominator marginal probabilities are intractable for the model used. We examine three approaches to integral approximation: Laplace approximation, Monte Carlo integration, and a Markov chain Monte Carlo (MCMC) ap...

Markov chain Monte Carlo (MCMC) is a popular and successful general-purpose tool for Bayesian inference. However, MCMC cannot be practically applied to large data sets because of the prohibitive cost of evaluating every likelihood term at every iteration. Here we present Firefly Monte Carlo (FlyMC) an auxiliary variable MCMC algorithm that only queries the likelihoods of a potentially small sub...

Multiple change-point models are a popular class of time series models which allow the description of temporal heterogeneity in data. We develop efficient Markov Chain Monte Carlo (MCMC) algorithms to perform Bayesian inference in this context. Our so-called Particle MCMC (PMCMC) algorithms rely on an efficient Sequential Monte Carlo (SMC) technique for change-point models, developed in [13], t...

We consider parallel asynchronous Markov Chain Monte Carlo (MCMC) sampling for problems where we can leverage (stochastic) gradients to define continuous dynamics which explore the target distribution. We outline a solution strategy for this setting based on stochastic gradient Hamiltonian Monte Carlo sampling (SGHMC) which we alter to include an elastic coupling term that ties together multipl...

Bayes Nets simplify probabilistic models, making it easy to work with these models. Unfortunately, sometimes people devise models that are too complicated to allow calculation of exact probabilities, so they instead use approximate inference, such as Markov Chain Monte Carlo (MCMC). However, MCMC can fail if the Bayes Net has zero-probability states that “disconnect” the state space. In this pa...

This paper presents Markov-Chain-Monte-Carlo (MCMC) procedures to sample uniformly from the collection of datasets that satisfy some revealed preference test. The MCMC for GARP test combines a Gibbs-sampler with simple hit and run step. It is shown has uniform distribution as its unique invariant it converges this at an exponential rate.

In this note we attempt to trace the history and development of Markov chain Monte Carlo (MCMC) from its early inception in the late 1940’s through its use today. We see how the earlier stages of the Monte Carlo research have led to the algorithms currently in use. More importantly, we see how the development of this methodology has not only changed our solutions to problems, but has changed th...

We focus on Bayesian variable selection in regression models. One challenge is to search the huge model space adequately, while identifying high posterior probability regions. In the past decades, the main focus has been on the use of Markov chain Monte Carlo (MCMC) algorithms for these purposes. In this article, we propose a new computational approach based on sequential Monte Carlo (SMC), whi...

b a c k g r o u n d & aim: the aim of the current study was to investigate the advantages of bayesian method in comparison to traditional methods to detect best antioxidant in freezing of human male gametes. methods  &  materials:   semen  samples  were  obtained  from  40  men  whose  sperm  had  normal criteria. a part of each sample was separated without antioxidant  as fresh and the remaini...