Markov Chain Monte Carlo (MCMC) methods are now an indispensable tool in scientific
computing. This book discusses recent developments of MCMC methods with an emphasis …

The Wang–Landau (WL) algorithm is an adaptive Markov chain Monte Carlo algorithm used
to calculate the spectral density for a physical system. A remarkable feature of the WL …

The problem of simulating from distributions with intractable normalizing constants has
received much attention in recent literature. In this article, we propose an asymptotic …

In autologistic regression models employed in the analysis of species' spatial distributions,
an additional explanatory variable, the autocovariate, is used to correct the effect of spatial …

The autologistic model is a Markov random field model for spatial binary data. Because it
can account for both statistical dependence among the data and for the effects of potential …

Sampling from the posterior distribution for a model whose normalizing constant is
intractable is a long-standing problem in statistical research. We propose a new algorithm …

Hidden Markov random fields represent a complex hierarchical model, where the hidden
latent process is an undirected graphical structure. Performing inference for such models is …

In long-lived species, individuals can skip reproduction. The proportion of breeders affects
population growth rate and viability, there is a need to investigate the factors influencing …

This paper complements the large body of social sensing literature by develo** means for
augmenting sensing data with inference results that" fill-in" missing pieces. Unlike trend …

Motivated by the spatial modeling of aberrant crypt foci (ACF) in colon carcinogenesis, we
consider binary data with probabilities modeled as the sum of a nonparametric mean plus a …