Monte Carlo methods and Markov Chain algorithms have long been central to computational science, forming the backbone of numerical simulation in a variety of disciplines. These techniques employ ...

We consider the recently introduced Transformation-based Markov Chain Monte Carlo (TMCMC) (Stat. Methodol. 16 (2014) 100–116), a methodology that is designed to update all the parameters ...

The Annals of Statistics, Vol. 39, No. 2 (April 2011), pp. 673-701 (29 pages) The random numbers driving Markov chain Monte Carlo (MCMC) simulation are usually modeled as independent U (0, 1) random ...

A research team from the University of British Columbia and Google has announced that they have developed a method called '3D Gaussian Splatting as a Markov Chain Monte Carlo Method' that dramatically ...

What Is Markov Chain Monte Carlo? Markov Chain Monte Carlo (MCMC) is a powerful technique used in statistics and various scientific fields to sample from complex probability distributions. It is ...

Brief review of conditional probability and expectation followed by a study of Markov chains, both discrete and continuous time. Queuing theory, terminology, and single queue systems are studied with ...

Start working toward program admission and requirements right away. Work you complete in the non-credit experience will transfer to the for-credit experience when you ...

The lognormal-gamma distribution, being a heavy-tailed distribution, is very attractive from an operational risk modeling perspective because historical operational losses also exhibit heavy tails.