We consider Bayesian hierarchical models for survival analysis, where the survival times are modeled through an underlying diffusion process, which determines the hazard rate. We show how these models can be efficiently treated by means of Markov chain Monte Carlo techniques.

The expectation of a function can be estimated by the empirical estimator based on the output of a Markov chain Monte Carlo method. We review results on the asymp-totic variance of the empirical estimator, and on improving the estimator by exploiting knowledge of the underlying distribution or of the transition distribution of the Markov chain.

February 25, 2015 Maintainer Benjamin M. Taylor License GPL-2 | GPL-3 Title Log-Gaussian Cox Process Type Package LazyLoad yes Author B. M. Taylor, T. M. Davies, B. S. Rowlingson, P. J. Diggle. Additional code contributions from E. Pebesma. Description Spatial and spatio-temporal modelling of point patterns using the log-Gaussian Cox process. Bayesian inference for s...

• Markov Chain Monte Carlo (MCMC) methods are used to perform a Bayesian analysis for interfailure data with constant hazard function in the presence of one or more change-points. We also present some Bayesian criteria to discriminate different models. The methodology is illustrated with a data set originally reported in Maguire, Pearson and Wynn [8].

A simple Monte-Carlo algorithm can be used to calculate Dempster-Shafer belief very efficiently unless the conflict between the evidences is very high. This paper introduces and explores Markov Chain Monte-Carlo algorithms for calculating Dempster-Shafer belief that can also work well when the conflict is high.

The problem of identifying the source from observations from a Poisson process can be encountered in fault diagnostics systems based on event counters. The identification of the inner state of the system must be made based on observations of counters which entail only information on the total sum of some events from a dual process which has made a transition from an intact to a broken state at ...

We present a functional central limit theorem for a general class of interacting Markov chain Monte Carlo interpretations of discrete generation measure-valued equations. The path space models associated with these stochastic processes belong to the class of nonlinear Markov chains interacting with their empirical occupation measures. We develop an original theoretical analysis based on resolve...