Probability distributions over many variables occur frequently in Bayesian inference, statistical physics and simulation studies. Samples from distributions give insight into their typical behavior and can allow approximation of any quantity of interest, such as expectations or normalizing constants. Markov chain Monte Carlo (MCMC), introduced by Metropolis et al. (1953), allows sampling from d...

a Faculty of Agriculture, Food & Natural Resources, The University of Sydney, NSW 2006, Australia b Department of Civil and Environmental Engineering, University of California, Irvine, 92697 CA, USA c Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, Nieuwe Achtergracht 166, 1018 WV, Amsterdam, The Netherlands d Earth and Environmental Sciences Division, Los Ala...

Markov chain Monte Carlo (e. g., the Metropolis algorithm and Gibbs sampler) is a general tool for simulation of complex stochastic processes useful in many types of statistical inference. The basics of Markov chain Monte Carlo are reviewed, including choice of algorithms and variance estimation, and some new methods are introduced. The use of Markov chain Monte Carlo for maximum likelihood est...

This paper presents the statistical inference on the parameters of the Burr type III distribution, when the data are Type-II hybrid censored. The maximum likelihood estimators are developed for the unknown parameters using the EM algorithm method. We provided the observed Fisher information matrix using the missing information principle which is useful for constructing the asymptotic confidence...

abstract background: keratoconus is a bilateral corneal disease, which one way to cure it is to transplant. the transplantation may be rejected by recipient's immune system, which leads to failure of the graft. this study aimed to analysis the factors affecting bilateral corneal graft rejection based on copula function. methods: a sample of bilateral graft rejection times was assessed. since co...