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Random variables can be discrete or continuous. Their associated probability distributions and statistical inference are major protagonists in data-science and also in Bayesian Learning, BL, a very timely topic due to the consideration of existing knowledge (priors) to determine posterior distributions, performing data-assimilation to enrich the knowledge. Some basic readings on probabilities, statistical inference and random processes, for completing the introduction addressed below are [1, 2].

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Random Variables: Probability, Statistics and Bayesian Learning

  • 章节

  • 出版模式：

    开放获取

  • 发表日期：

    2025年

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  • Francisco Chinesta,
  • Elías Cueto,
  • Victor Champaney,
  • Chady Ghnatios,
  • Amine Ammar,
  • Nicolas Hascoët,
  • David González,
  • Icíar Alfaro,
  • Daniele Di Lorenzo,
  • Angelo Pasquale,
  • Dominique Baillargeat

摘要

Random variables can be discrete or continuous. Their associated probability distributions and statistical inference are major protagonists in data-science and also in Bayesian Learning, BL, a very timely topic due to the consideration of existing knowledge (priors) to determine posterior distributions, performing data-assimilation to enrich the knowledge. Some basic readings on probabilities, statistical inference and random processes, for completing the introduction addressed below are [1, 2].