Markov Chain Monte Carlo Simulation Methods for Structural Reliability Analysis

Markov Chain Monte Carlo Simulation Methods in Econometrics

We present several Markov chain Monte Carlo simulation methods that have been widely used in recent years in econometrics and statistics. Among these is the Gibbs sampler, which has been of particular interest to econometricians. Although the paper summarizes some of the relevant theoretical literature, its emphasis is on the presentation and explanation of applications to important models that...

Bayesian Updating of Structural Models and Reliability using Markov Chain Monte Carlo Simulation

In a full Bayesian probabilistic framework for ‘‘robust’’ system identification, structural response predictions and performance reliability are updated using structural test data D by considering the predictions of a whole set of possible structural models that are weighted by their updated probability. This involves integrating h(u)p(uuD) over the whole parameter space, where u is a parameter...

Markov Chain Monte Carlo Simulation Made Simple

Sequentially Interacting Markov Chain Monte Carlo Methods

We introduce a novel methodology for sampling from a sequence of probability distributions of increasing dimension and estimating their normalizing constants. These problems are usually addressed using Sequential Monte Carlo (SMC) methods. The alternative Sequentially Interacting Markov Chain Monte Carlo (SIMCMC) scheme proposed here works by generating interacting non-Markovian sequences which...

Markov chain Monte Carlo methods in biostatistics.

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 techniques. Markov chain Monte Carlo (MCMC) methods are an important set of tools for such simulations. We give an overview and references of this rapidly emerging technology along with a relatively simple example. MCM...