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This interpretation can be seen as a refinement of Bayesian networks.<\/jats:p><jats:p>Bayesian strategies are based on a new form of <jats:italic>event structure<\/jats:italic>, 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.<\/jats:p><jats:p>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.<\/jats:p>","DOI":"10.1007\/978-3-030-72019-3_19","type":"book-chapter","created":{"date-parts":[[2021,3,22]],"date-time":"2021-03-22T14:03:10Z","timestamp":1616421790000},"page":"519-547","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Bayesian strategies: probabilistic programs as generalised graphical models"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8192-0321","authenticated-orcid":false,"given":"Hugo","family":"Paquet","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,3,23]]},"reference":[{"key":"19_CR1","doi-asserted-by":"crossref","unstructured":"Abbes, S., Benveniste, A.: True-concurrency probabilistic models: Branching cells and distributed probabilities for event structures. 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