Sampling from a polytope and hard-disk Monte Carlo

Christiane Lemieux Monte Carlo and Quasi - Monte Carlo Sampling

Prototype Selection from Homogeneous Subsets by a Monte Carlo Sampling

In order to reduce computatiomd ald storage c~sts of learning methods, we present a protot.,~T)e selection algorithm. This apl)roach uses il,formation contained in a connected neighborhood graph. It determines the number of homogeneous subsets in the Rp space, and uses it to fix the number of prototypes in advance. Once this number is determined, we. idcntii~," prototypes applying a stratified ...

Stratified sampling and quasi-Monte Carlo simulation

We provide a method for the generation of paths of Lévy processes which has many of the benefits that the Brownian bridge construction has for Brownian motion. We show how, using our method, one can apply stratified sampling and quasi-Monte Carlo methods to obtain better numerical schemes analog to the Brownian case. As a numerical example we consider the problem of pricing an asian option in t...

Markov Chain Monte Carlo and Gibbs Sampling

A major limitation towards more widespread implementation of Bayesian approaches is that obtaining the posterior distribution often requires the integration of high-dimensional functions. This can be computationally very difficult, but several approaches short of direct integration have been proposed (reviewed by Smith 1991, Evans and Swartz 1995, Tanner 1996). We focus here on Markov Chain Mon...

Markov Chain Monte Carlo and Gibbs Sampling

A major limitation towards more widespread implementation of Bayesian approaches is that obtaining the posterior distribution often requires the integration of high-dimensional functions. This can be computationally very difficult, but several approaches short of direct integration have been proposed (reviewed by Smith 1991, Evans and Swartz 1995, Tanner 1996). We focus here on Markov Chain Mon...