Variance estimation and ranking methods are developed for stochastic processes modeled by Gaussian mixture distributions. It is shown that the variance estimate from a Gaussian mixture distribution has the same properties as a variance estimate from a single Gaussian distribution based on a reduced number of samples. Hence, well known tools of variance estimation and ranking of single Gaussian ...

Monte Carlo methods, in particular those based on Markov chains and on interacting particle systems, are by now tools that are routinely used in machine learning. These methods have had a profound impact on statistical inference in a wide range of application areas where probabilistic models are used. Moreover, there are many algorithms in machine learning which are based on the idea of process...

While parallel computers ooer signiicant computational performance, it is generally necessary to evaluate several programming strategies. Two programming strategies for a fairly common problem|a periodic tridiagonal solver|are developed and evaluated. Simple model calculations as well as timing results are presented to evaluate these strategies. The particular tridiagonal solver evaluated is us...

Regenerative simulation (RS) is a method of stochastic steady-state simulation in which output data are collected and analysed within regenerative cycles (RCs). Since data collected during consecutive RCs are independent and identically distributed, there is no problem with the initial transient period in simulated processes, which is a perennial issue of concern in all other types of steady-st...