A guide to simulation (2nd ed.)
A guide to simulation (2nd ed.)
Strong uniform times and finite random walks
Advances in Applied Mathematics
The asymptotic efficiency of simulation estimators
Operations Research
Principles of Discrete Event Simulation
Principles of Discrete Event Simulation
Simulation Modeling and Analysis
Simulation Modeling and Analysis
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Efficient stopping rules for Markov chains
STOC '95 Proceedings of the twenty-seventh annual ACM symposium on Theory of computing
Generating random spanning trees more quickly than the cover time
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
An interruptible algorithm for perfect sampling via Markov chains
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Proceedings of the seventh annual ACM-SIAM symposium on Discrete algorithms
Convergence assessment techniques for Markov chain Monte Carlo
Statistics and Computing
Exact Sampling in Machine Scheduling Problems
APPROX '01/RANDOM '01 Proceedings of the 4th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems and 5th International Workshop on Randomization and Approximation Techniques in Computer Science: Approximation, Randomization and Combinatorial Optimization
Limit Theory for Taboo-Regenerative Processes
Queueing Systems: Theory and Applications
Initial transient problem for steady-state output analysis
WSC '05 Proceedings of the 37th conference on Winter simulation
Learning from uniformly ergodic Markov chains
Journal of Complexity
ACM Transactions on Modeling and Computer Simulation (TOMACS)
The computational complexity of estimating MCMC convergence time
APPROX'11/RANDOM'11 Proceedings of the 14th international workshop and 15th international conference on Approximation, randomization, and combinatorial optimization: algorithms and techniques
Exact simulation of the stationary distribution of the FIFO M/G/c queue: the general case for ρ
Queueing Systems: Theory and Applications
Nonexistence of a class of variate generation schemes
Operations Research Letters
Rethinking the initialization bias problem in steady-state discrete event simulation
Proceedings of the Winter Simulation Conference
Proceedings of the Winter Simulation Conference
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Let X = {X(t)}t ≥ 0 be a stochastic process with a stationary version X*. It is investigated when it is possible to generate by simulation a version X˜ of X with lower initial bias than X itself, in the sense that either X˜ is strictly stationary (has the same distribution as X*) or the distribution of X˜ is close to the distribution of X*. Particular attention is given to regenerative processes and Markov processes with a finite, countable, or general state space. The results are both positive and negative, and indicate that the tail of the distribution of the cycle length &tgr; plays a critical role. The negative results essentially state that without some information on this tail, no a priori computable bias reduction is possible; in particular, this is the case for the class of all Markov processes with a countably infinite state space. On the contrary, the positive results give algorithms for simulating X˜ for various classes of processes with some special structure on &tgr;. In particular, one can generate X˜ as strictly stationary for finite state Markov chains, Markov chains satisfying a Doeblin-type minorization, and regenerative processes with the cycle length &tgr; bounded or having a stationary age distribution that can be generated by simulation.