Randomized quasi-Monte Carlo (RQMC) can be seen as a variance reduction method that provides an unbiased estimator of the integral of a function <i>f</i> ov...

Advanced discrete choice models, such as parametric/non-parametric mixed logit and hybrid choice models, are heavily used in travel behavior research. The...

We study the problem of constructing shifted rank-1 lattice rules for the approximation of high-dimensional integrals with a low weighted star discrepancy, f...

We study the convergence behavior of a randomized quasi-Monte Carlo (RQMC) method for the simulation of discrete-time Markov chains, known as array-RQMC. The...

We consider a class of Markov chain models that includes the highly reliable Markovian systems (HRMS) often used to represent the evolution of multicomponent...

We review the basic principles of Quasi-Monte Carlo (QMC) methods, the randomizations that turn them into variance-reduction techniques, the integration erro...

Staffing and scheduling optimization in large multiskill call centers is time-consuming, mainly because it requires lengthy simulations to evaluate performan...

<p>Monte Carlo simulation is an incredibly versatile tool for studying complex stochastic systems. By replicating the simulation several times independent...

For every stochastic simulation model, there is in theory a way of changing the probability laws that drive the system so that the resulting IS estimator h...

The asymptotic robustness of estimators as a function of a rarity parameter, in the context of rare-event simulation, is often qualified by properties such a...

The coupling-from-the-past (CFTP) algorithm of Propp and Wilson, also called perfect sampling, permits one to sample exactly from the stationary distributio...

We examine and compare simulation-based algorithms for solving the agent scheduling problem in a multiskill call center. This problem consists in minimizing...

Random number generators based on linear recurrences modulo 2 are among the fastest long-period generators currently available. The uniformity and independe...

We propose and analyze a quasi-Monte Carlo (QMC) method for simulating a discrete-time Markov chain on a discrete state space of dimension <img src="/cgi-...

Starting from coding-theoretic constructions, we build digital nets with good figures of merit, where the figure of merit takes into account the equidistrib...

Variance reduction techniques (VRTs) are often essential to make simulation quick and accurate enough to be useful. A case in point is simulation-based opti...

A popular approach for modeling dependence in a finite-dimensional random vector <b>X</b> with given univariate marginals is via a normal copula that fits t...

We introduce <i>TestU01</i>, a software library implemented in the ANSI C language, and offering a collection of utilities for the empirical statistical te...

We introduce and study a randomized quasi-Monte Carlo method for estimating the state distribution at each step of a Markov chain. The number of steps in th...

Importance sampling (IS) is the primary technique for constructing reliable estimators in the context of rare-event simulation. The asymptotic robustness of...