This paper proposes a software random testing scheme based on Markov chain Monte Carlo (MCMC) method. The significant issue of software testing is how to use the prior knowledge of experienced testers and the information obtained from the preceding test outcomes in making test cases. The concept of Markov chain Monte Carlo random testing (MCMCRT) is based on the Bayes approach to parametric mod...

In this paper, we present an adaptive evolutionary Monte Carlo algorithm (AEMC), which combines a treebased predictive model with an evolutionary Monte Carlo sampling procedure for the purpose of global optimization. Our development is motivated by sensor placement applications in engineering, which requires optimizing certain complicated “black-box” objective function. The proposed method is a...

Markov Chain Monte Carlo (MCMC) is a technique for sampling from a target probability distribution, and has risen in importance as faster computing hardware has made possible the exploration of hitherto difficult distributions. Unfortunately, this powerful technique is often misapplied by poor selection of transition kernel for the Markov chain that is generated by the simulation. Some kernels ...

Probabilistic models are conceptually powerful tools for finding structure in data, but their practical effectiveness is often limited by our ability to perform inference in them. Exact inference is frequently intractable, so approximate inference is often performed using Markov chain Monte Carlo (MCMC). To achieve the best possible results from MCMC, we want to efficiently simulate many steps ...

MCMCstuff toolbox is a collection of Matlab functions for Bayesian inference with Markov chain Monte Carlo (MCMC) methods. This documentation introduces some of the features available in the toolbox. Introduction includes demonstrations of using Bayesian Multilayer Perceptron (MLP) network and Gaussian process in simple regression and classification problems with a hierarchical automatic releva...

In the following paper we investigate simulation methodology for Bayesian inference in Lévy driven SV models. Typically, Bayesian inference from such statistical models is performed using Markov chain Monte Carlo (MCMC) methods. However, it is well-known that fitting SV models using MCMC is not always straight-forward. One method that can improve over MCMC is SMC samplers ([14]), but in that ap...

One of the main shortcomings of Markov chain Monte Carlo samplers is their inability to mix between modes of the target distribution. In this paper we show that advance knowledge of the location of these modes can be incorporated into the MCMC sampler by introducing mode-hopping moves that satisfy detailed balance. The proposed sampling algorithm explores local mode structure through local MCMC...

Adaptive Markov Chain Monte Carlo (MCMC) algorithms attempt to ‘learn’ from the results of past iterations so the Markov chain can converge quicker. Unfortunately, adaptive MCMC algorithms are no longer Markovian, so their convergence is difficult to guarantee. In this paper, we develop new diagnostics to determine whether the adaption is still improving the convergence. We present an algorithm...

Solving inverse problems in a complex, geologically realistic, and discrete model space and from a sparse set of observations is a very challenging task. Extensive exploration by Markov chain Monte Carlo (McMC) methods often results in considerable computational efforts. Most optimization methods, on the other hand, are limited to linear (continuous) model spaces and the minimization of an obje...