A method is suggested to estimate posterior model probabilities and model averaged
parameters via MCMC sampling under a Bayesian approach. The estimates use pooled …

Recent developments in Bayesian computing allow accurate estimation of integrals, making
advanced Bayesian analysis feasible. However, some problems remain difficult, such as …

Microsimulation models are an important tool for estimating the potential behavioral and
economic effects of public policies on decision making units, including individuals and …

We propose a Bayesian model selection approach for generalized linear mixed models
(GLMMs). We consider covariance structures for the random effects that are widely used in …

ABSTRACT A computationally simple approach to inference in state space models is
proposed, using approximate Bayesian computation (ABC). ABC avoids evaluation of an …

In Bayesian inference, predictive distributions are typically in the form of samples generated
via Markov chain Monte Carlo or related algorithms. In this paper, we conduct a systematic …

Approximate Bayesian computation (ABC) has advanced in two decades from a seminal
idea to a practically applicable inference tool for simulator‐based statistical models, which …

Objective. Uncertainty analysis (UA) is an important part of simulation model validation.
However, literature is imprecise as to how UA should be performed in the context of …

The marked increase in popularity of Bayesian methods in statistical practice over the last
decade owes much to the simultaneous development of Markov chain Monte Carlo (MCMC) …

Appropriate models in biostatistics are often quite complicated. Such models are typically
most easily fit using Bayesian methods, which can often be implemented using simulation …