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Everything about Importance sampling

Distribution estimation technique

Importance sampling is a Monte Carlo method for evaluating properties of a particular distribution, while only having samples generated from a different distribution than the distribution of interest. Its introduction in statistics is generally attributed to a paper by Teun Kloek and Herman K. van Dijk in 1978, but its precursors can be found in statistical physics as early as 1949. Importance sampling is also related to umbrella sampling in computational physics. Depending on the application, the term may refer to the process of sampling from this alternative distribution, the process of inference, or both. Wikipedia →

🧭 Start here: a 6-step reading path

  1. Read the overview: Importance samplingA 5-minute orientation
  2. Skim a recent paper: A review and assessment of importance sampling methods for reliability analysisInfluential work since 2022
  3. Pick up a classic book: International convention to facilitate the importation of commercial samples and advertising materialThe most-reprinted book on the subject
  4. Read the landmark paper: Annealed importance samplingCited 780 times
  5. Go sideways: Outline of statisticsA closely related topic
  6. Go sideways: Markov chain Monte CarloA closely related topic

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🏆 Landmark papers (most cited)

🆕 Influential since 2022

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