Abstract
The classical Monte Carlo Markov chain method of Chapter provides an approximate sample of a probability distribution \(\pi \) on a finite state space E. Chapter gives ways of measuring the accuracy of such an approximate sample in terms of its variation distance from the target distribution. The goal is now to construct an exact sample of \(\pi \), that is, a random variable Z such that \(P(Z=i) =\pi (i)\) for all \(i\in E\).
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Brémaud, P. (2017). Exact Sampling. In: Discrete Probability Models and Methods. Probability Theory and Stochastic Modelling, vol 78. Springer, Cham. https://doi.org/10.1007/978-3-319-43476-6_21
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DOI: https://doi.org/10.1007/978-3-319-43476-6_21
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Publisher Name: Springer, Cham
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Online ISBN: 978-3-319-43476-6
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