Abstract Markov chain Monte Carlo (MCMC) is a method of producing a correlated sample to
estimate characteristics of a target distribution. A fundamental question is how long should …

We consider the problem of accounting for model uncertainty in linear regression models.
Conditioning on a single selected model ignores model uncertainty, and thus leads to the …

Many scientific problems require calibrating a set of model parameters to fit a set of data.
Various approaches exist for performing this calibration, but not all of them account for …

We investigate the effects of measurement error on the estimation of nonparametric variance
functions. We show that either ignoring measurement error or direct application of the …

Markov chain Monte Carlo (MCMC) is an essential set of tools for estimating features of
probability distributions commonly encountered in modern applications. For MCMC …

Publisher Summary This chapter is about MCMC methods and their use within the Bayesian
framework. It points out that MCMC can be quite helpful also within the frequentist approach …

SUMMARY Markov chain Monte Carlo produces a correlated sample which may be used for
estimating expectations with respect to a target distribution. A fundamental question is: when …

We propose model-based inference for differential gene expression, using a nonparametric
Bayesian probability model for the distribution of gene intensities under various conditions …

Background: Mathematical models are commonly used to predict future benefits of new
therapies or interventions in the healthcare setting. The reliability of model results is greatly …

We introduce MCMCpack, an R package that contains functions to perform Bayesian
inference using posterior simulation for a number of statistical models. In addition to code …