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arXiv 2503.02477math.CTcs.LOcs.PLmath.PR
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随机变量、条件独立性与抽象样本空间的范畴

Random Variables, Conditional Independence and Categories of Abstract Sample Spaces

Dario Stein

AI总结:

本文将 Simpson 的概率层与 Markov 范畴结合,证明合适的 Markov 范畴可构造满足其公理的样本空间范畴,从而纯综合地发展随机变量与条件独立性理论。

摘要:

概率论已经出现了两种高层“图景”:一种以随机变量概念为核心,另一种则关注分布和概率通道(Markov核)。尽管基于通道的图景已借助 Markov 范畴概念得到成功公理化并被广泛推广,但随机变量图景的范畴语义仍不够清晰。Simpson 的概率层是一种近期方法,其中随机变量等概率概念被允许在样本空间的一个 site 上变化。Simpson 已识别出这些 site 上的丰富结构,尤其是抽象的条件独立性概念,并给出了从数据库上的概率到 nominal sets 等一系列例子。我们的目标是将这一进展与 Markov 范畴的一般性和抽象性结合起来:我们证明,对于任意合适的 Markov 范畴,都可以定义一个满足 Simpson 公理的样本空间范畴,并且可以在该设定下以纯综合的方式发展概率层理论。我们从众所周知的 Markov 范畴出发,以统一方式恢复 Simpson 的例子,并考虑进一步的推广。

英文摘要:

Two high-level "pictures" of probability theory have emerged: one that takes as central the notion of random variable, and one that focuses on distributions and probability channels (Markov kernels). While the channel-based picture has been successfully axiomatized, and widely generalized, using the notion of Markov category, the categorical semantics of the random variable picture remain less clear. Simpson's probability sheaves are a recent approach, in which probabilistic concepts like random variables are allowed vary over a site of sample spaces. Simpson has identified rich structure on these sites, most notably an abstract notion of conditional independence, and given examples ranging from probability over databases to nominal sets. We aim bring this development together with the generality and abstraction of Markov categories: We show that for any suitable Markov category, a category of sample spaces can be defined which satisfies Simpson's axioms, and that a theory of probability sheaves can be developed purely synthetically in this setting. We recover Simpson's examples in a uniform fashion from well-known Markov categories, and consider further generalizations.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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