An overview of ordering defined on the space of Markov chains having a pre-specified distribution as their unique stationary distribution is provided. The intuition gained by studying these orderings is used to improve existing Markov chain Monte Carlo algorithms.

A new acceleration algorithm to address the problem of multiple time scales in variational Monte Carlo simulations is presented. Core electrons usually require smaller time steps than valence electrons. After a first attempted move has been rejected, the delayed rejection algorithm attempts a...

The class of finite state space Markov chains stationary with respect to a common pre-specified distribution is considered. An easy to check partial ordering is defined on this class. The ordering provides a sufficient condition for the dominating Markov chain to be more efficient. Efficiency is...

Peskun ordering is a partial ordering defined on the space of transition matrices of discrete time Markov chains. If the Markov chains are reversible with respect to a common stationary distribution "greek Pi", Peskun ordering implies an ordering on the asymptotic variances of the resulting...

The class of Metropolis-Hastings algorithms can be modified by delaying the rejection of proposed moves. The new samplers are proved to perform better than the original ones in terms of asymptotic variance of the estimates on a sweep by sweep basis. The delaying rejection algorithms also allow...

We develop a Bayesian hierarchical logistic regression model to predict the credit risk of companiers classified in different sectors. Explanatory variables derived by experts from balance-sheets are included. Markov chain Monte Carlo (MCMC) methods are used to estimate the proposed model. In...

As long-time enthusiast for auxiliary variables methods in Bayesian MCMC, we are glad to have the opportunity to discuss this interesting paper.The authors have a reputation for picturesque and metaphorical titles, and this paper is no exception. We were intrigued by the 'artistic' aspirations...

We exend Meng and Wong (1996) identity from a fixed to a varying dimentional setting. The identity is a very powerful tool to estimate ratios of normalizing constants and thus can be used to evaluate Bayes factors. The extention is driven by the reversibler jump algorithm so that the output from...

If T is the coalescence time of the Propp and Wilson, perfect simulation algorithm, the aim of this paper is to show that T depends on the second largest eigenvalue modulus of the transition matrix of the underlying Markov chain. This gives a relationship between the ordering based on the speed...

In this paper we propose a Bayesian Latent Class model for capture-recapture data. Through two appliations, the first concerning a sample of snowshoe hares and the second concerning a sample of diabetics in a small Italian town, we show how the proposed approach may be effectively used to obtain...