We present a new Bayesian method for estimating demographic and phylogenetic history using population genomic data. Several key innovations are introduced that allow the study of diverse models within an Isolation-with-Migration framework. The new method implements a 2-step analysis, with an initial Markov chain Monte Carlo (MCMC) phase that samples simple coalescent trees, followed by the calc...

This paper introduces a new BRDF sampling method with reduced variance, which is based on a hierarchical adaptive parameterless PDF. This PDF is based also on rejection sampling with a bounded average number of trials, even in regions where the BRDF does exhibit high variations. Our algorithm works in an appropiate way with both physical and analytical reflectance models. Reflected directions a...

These lectures give an introduction to Monte Carlo simulations of classical statistical physics systems and their statistical analysis. After briefly recalling a few elementary properties of phase transitions, the concept of importance sampling Monte Carlo methods is discussed and illustrated by a few standard local update algorithms (Metropolis, heat-bath, Glauber). Then emphasis is placed on ...

A brief introduction to the technique of Monte Carlo for simulation of statistical physics problems is presented. Ising spin model is taken as an example. The topics covered include random and pseudo random numbers, random sampling techniques, Markov chain, Metropolis algorithm, continuous phase transition, statistical errors from correlated and uncorrelated data, finite size scaling, critical ...