A critical issue for users of Markov chain Monte Carlo (MCMC) methods in applications is
how to determine when it is safe to stop sampling and use the samples to estimate …

Video traffic is widely expected to account for a large portion of the traffic in future wireline
and wireless networks, as multimedia applications are becoming increasingly popular …

The goal of this fifth edition of Simulation Modeling and Analysis remains the same as that
for the first four editions: to give a comprehensive and state-of-the-art treatment of all the …

Praise for the Third Edition" This is one of the best books available. Its excellent
organizational structure allows quick reference to specific models and its clear …

This paper studies the estimation of the steady state mean of an output sequence from a
discrete event simulation. It considers the problem of the automatic generation of a …

Despite increasing interest in Bayesian approaches, especially across the social sciences, it
has been virtually impossible to find a text that introduces Bayesian data analysis in a …

Changes and additions are sprinkled throughout. Among the significant new features are:•
Markov-chain simulation (Sections 1. 3, 2. 6, 3. 6, 4. 3, 5. 4. 5, and 5. 5);• gradient estimation …

MONTE CARLO METHODS IN STATISTICAL MECHANICS: FOUNDATIONS AND NEW
ALGORITHMS Page 1 6 MONTE CARLO METHODS IN STATISTICAL MECHANICS …

The pivot algorithm is a dynamic Monte Carlo algorithm, first invented by Lal, which
generates self-avoiding walks (SAWs) in a canonical (fixed-N) ensemble with free endpoints …

Markov chain Monte Carlo (MCMC) is the most widely used method of estimating joint
posterior distributions in Bayesian analysis. The idea of MCMC is to iteratively produce …