We propose an adaptive independent Metropolis–Hastings algorithm with the ability to learn from all previous proposals in the chain except the current location. It is an extension of the independent Metropolis–Hastings algorithm. Convergence is proved provided a strong Doeblin condition is satisfied, which essentially requires that all the proposal functions have uniformly heavier tails than th...

Classifiers based on Bayesian networks are usually learned with a fixed structure or a small subset of possible structures. In the presence of unlabeled data this strategy can be detrimental to classification performance, when the assumed classifier structure is incorrect. In this paper we present a classification driven learning method for Bayesian network classifiers that is based on Metropol...

In this paper we present a Bayesian analysis of location-scale regression models assuming standard lifetime distributions and an additional error term with a mixture of normal distributions. Considering a censored lifetime data set, we use Gibbs sampling with Metropolis-Hastings algorithm to get Bayesian quantities of interest. The proposed regression model gives a great flexibility to fit life...

The stability and ergodicity properties of an adaptive random walk Metropolis algorithm are considered. The algorithm adjusts the scale of the symmetric proposal distribution continuously based on the observed acceptance probability. Unlike the previously proposed forms of this algorithm, the adapted scaling parameter is not constrained within a predefined compact interval. This makes the algor...

This paper addresses pixel-level segmentation of a human body from a single image. The problem is formulated as a multi-region segmentation where the human body is constrained to be a collection of geometrically linked regions and the background is split into a small number of distinct zones. We solve this problem in a Bayesian framework for jointly estimating articulated body pose and the pixe...

1 Applied Mathematics Research Centre, Coventry University, Coventry CV1 5FB, UK [email protected] 2 Physikalisches Institut, Universität Freiburg, 79104 Freiburg, Germany 3 Ivan Franko National University of Lviv, 79005 Lviv, Ukraine 4 Institute for Condensed Matter Physics of the National Academy of Sciences of Ukraine, 79011 Lviv, Ukraine 5 Institut für Theoretische Physik, Johannes...

A proper choice of a proposal distribution for MCMC methods, e.g. for the Metropolis-Hastings algorithm, is well known to be a crucial factor for the convergence of the algorithm. In this paper we introduce an adaptive Metropolis Algorithm (AM), where the Gaussian proposal distribution is updated along the process using the full information cumulated so far. Due to the adaptive nature of the pr...

A proper choice of a proposal distribution for Markov chain Monte Carlo methods, for example for the Metropolis±Hastings algorithm, is well known to be a crucial factor for the convergence of the algorithm. In this paper we introduce an adaptive Metropolis (AM) algorithm, where the Gaussian proposal distribution is updated along the process using the full information cumulated so far. Due to th...