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Titlebook: Automatic Nonuniform Random Variate Generation; Wolfgang Hörmann,Josef Leydold,Gerhard Derflinger Book 2004 Springer-Verlag Berlin Heidelb

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Markov Chain Monte Carlo Methodspractical point of view limited to small dimensions up to at most 10. And there are lots of distributions that are even difficult to sample from in dimension three or four. A totally different approach is based on the fact that we always can easily construct a Markov chain that has the desired fixed
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Introductionible” of random variate generation. We can certainly say that random variate generation has become an accepted research area considered as a subarea of statistical computing and simulation methodology. Practically all text-books on discrete event simulation or Monte Carlo methods include at least on
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Transformed Density Rejection (TDR) has been published by Gilks and Wild (1992) under the name .. The use of a general transformation and the name . was suggested by Hörmann (1995). In this presentation we try to develop the idea such that the reader can also see the (in our opinion) “beautiful” mathematical background (Sects. 4.2–4.
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Li-Shih Huang,Raj Khatri,Amjad Alhemaidor random vector generation. For many of these families the marginals are known as well. The monograph of Johnson (1987) is presenting this branch of multivariate simulation. You can also find many of these distributions in Devroye (1986a, Chap. XI).
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