Statistical signal processing applications usually require the estimation of some parameters
of interest given a set of observed data. These estimates are typically obtained either by …

A critical issue for users of Markov chain Monte Carlo (MCMC) methods in applications is
how to determine when it is safe to stop sampling and use the samples to estimate …

The most user-friendly and authoritative resource on missing data has been completely
revised to make room for the latest developments that make handling missing data more …

General state-space Markov chain theory has evolved to make it both more accessible and
more powerful. Markov Chain Monte Carlo in Practice introduces MCMC methods and their …

We generalize the method proposed by Gelman and Rubin (1992a) for monitoring the
convergence of iterative simulations by comparing between and within variances of multiple …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

Computer-intensive algorithms, such as the Gibbs sampler, have become increasingly
popular statistical tools, both in applied and theoretical work. The properties of such …

This book provides a self-contained and up-to-date treatment of the Monte Carlo method
and develops a common framework under which various Monte Carlo techniques can be" …

Several Markov chain methods are available for sampling from a posterior distribution. Two
important examples are the Gibbs sampler and the Metropolis algorithm. In addition, several …

It's been over a decade since the first edition of Measurement Error in Nonlinear Models
splashed onto the scene, and research in the field has certainly not cooled in the interim. In …