We summarize the results of an experimental performance evaluation of using an empirical histogram to approximate the steady-state distribution of the underlying stochastic proces...
Adaptive Monte Carlo methods are specialized Monte Carlo simulation techniques where the methods are adaptively tuned as the simulation progresses. The primary focus of such techn...
We review the basic principles of Quasi-Monte Carlo (QMC) methods, the randomizations that turn them into variancereduction techniques, and the main classes of constructions under...
A classical problem of stochastic simulation is how to estimate the variance of the sample mean of dependent but stationary outputs. Many variance estimators, such as the batch me...
We formalize a model for supervised learning of action strategies in dynamic stochastic domains and show that PAC-learning results on Occam algorithms hold in this model as well. W...