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- W3131289200 abstract "Importance sampling (IS) is a Monte Carlo technique for the approximation of intractable distributions and integrals with respect to them. The origin of IS dates from the early 1950s. In the past decades, the rise of the Bayesian paradigm and the increase of the available computational resources have propelled the interest in this theoretically sound methodology. In this article, we first describe the basic IS algorithm and then revisit the recent advances in this methodology. We pay particular attention to two sophisticated lines. First, we focus on multiple IS (MIS), the case where more than one proposal is available. Second, we describe adaptive IS (AIS), the generic methodology for adapting one or more proposals." @default.
- W3131289200 created "2021-03-01" @default.
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- W3131289200 date "2021-02-17" @default.
- W3131289200 modified "2023-10-18" @default.
- W3131289200 title "Advances in Importance Sampling" @default.
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- W3131289200 doi "https://doi.org/10.1002/9781118445112.stat08284" @default.
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