Importance sampling for stochastic simulations
Management Science
Likelihood ratio gradient estimation for stochastic systems
Communications of the ACM - Special issue on simulation
Bias properties of budget constrained simulations
Operations Research
The asymptotic efficiency of simulation estimators
Operations Research
A Unified Framework for Simulating Markovian Models of Highly Dependable Systems
IEEE Transactions on Computers
Fast simulation of rare events in queueing and reliability models
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Using permutations in regenerative simulations to reduce variance
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on modeling and analysis of stochastic systems
The balanced likelihood ratio method for estimating performance measures of highly reliable systems
Proceedings of the 30th conference on Winter simulation
Likelilood ratio gradient estimation: an overview
WSC '87 Proceedings of the 19th conference on Winter simulation
Regenerative steady-state simulation of discrete-event systems
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Steady state simulation analysis: importance sampling using the semi-regenerative method
Proceedings of the 33nd conference on Winter simulation
Central Limit Theorems for Permuted Regenerative Estimators
Operations Research
SIMULATION OF PROCESSES WITH MULTIPLE REGENERATION SEQUENCES
Probability in the Engineering and Informational Sciences
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
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We develop a class of techniques for analyzing the output of simulations of a semi-regenerative process. Called the semi-regenerative method, the approach is a generalization of the regenerative method, and it can increase efficiency. We consider the estimation of various performance measures, including steady-state means, expected cumulative reward until hitting a set of states, derivatives of steady-state means, and time-average variance constants. We also discuss importance sampling and a bias-reduction technique. In each case, we develop two estimators: one based on a simulation of a single sample path, and the other a type of stratified estimator in which trajectories are generated in an independent and identically distributed manner. We establish a central limit theorem for each estimator so confidence intervals can be constructed.