Bayesian strategies: probabilistic programs as generalised graphical models

Author:

Paquet HugoORCID

Abstract

AbstractWe introduce Bayesian strategies, a new interpretation of probabilistic programs in game semantics. This interpretation can be seen as a refinement of Bayesian networks.Bayesian strategies are based on a new form of event structure, with two causal dependency relations respectively modelling control flow and data flow. This gives a graphical representation for probabilistic programs which resembles the concrete representations used in modern implementations of probabilistic programming.From a theoretical viewpoint, Bayesian strategies provide a rich setting for denotational semantics. To demonstrate this we give a model for a general higher-order programming language with recursion, conditional statements, and primitives for sampling from continuous distributions and trace re-weighting. This is significant because Bayesian networks do not easily support higher-order functions or conditionals.

Publisher

Springer International Publishing

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Zeta Functions and the (Linear) Logic of Markov Processes;Logical Methods in Computer Science;2024-08-29

2. Higher Order Bayesian Networks, Exactly;Proceedings of the ACM on Programming Languages;2024-01-05

3. A linear exponential comonad in s-finite transition kernels and probabilistic coherent spaces;Information and Computation;2023-12

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