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Culbertson, J., & Sturtz, K. (2013). Bayesian machine learning via category theory. ArXiv:1312.1445 [Math]. Retrieved from http://arxiv.org/abs/1312.1445
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Heckerman, D. (1995). A Tutorial on Learning With Bayesian Networks. Retrieved from https://www.microsoft.com/en-us/research/publication/a-tutorial-on-learning-with-bayesian-networks/
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Jacobs, B. (2018). Categorical Aspects of Parameter Learning. ArXiv:1810.05814 [Cs]. Retrieved from http://arxiv.org/abs/1810.05814
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Jacobs, B., & Adams, R. (2015). A Type Theory for Probabilistic and Bayesian Reasoning. ArXiv:1511.09230 [Cs, Math]. Retrieved from http://arxiv.org/abs/1511.09230
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Jacobs, B., & Cho, K. (2019). Disintegration and Bayesian Inversion via String Diagrams. Mathematical Structures in Computer Science, 29(7), 938–971. https://doi.org/10/ggdf9v
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Jacobs, B., Kissinger, A., & Zanasi, F. (2019). Causal Inference by String Diagram Surgery. ArXiv:1811.08338 [Cs, Math]. Retrieved from http://arxiv.org/abs/1811.08338
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Jacobs, B., & Zanasi, F. (2017). A Formal Semantics of Influence in Bayesian Reasoning. Schloss Dagstuhl - Leibniz-Zentrum Fuer Informatik GmbH, Wadern/Saarbruecken, Germany. https://doi.org/10/ggdgbc
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Jacobs, B., & Zanasi, F. (2016). A Predicate/State Transformer Semantics for Bayesian Learning. Electronic Notes in Theoretical Computer Science, 325, 185–200. https://doi.org/10/ggdgbb
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Jacobs, B., & Zanasi, F. (2018). The Logical Essentials of Bayesian Reasoning. ArXiv:1804.01193 [Cs]. Retrieved from http://arxiv.org/abs/1804.01193
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McCullagh, P. (2002). What is a statistical model? The Annals of Statistics, 30(5), 1225–1310. https://doi.org/10/bkts3m
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Staton, S., Yang, H., Heunen, C., Kammar, O., & Wood, F. (2016). Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints. Proceedings of the 31st Annual ACM/IEEE Symposium on Logic in Computer Science - LICS ’16, 525–534. https://doi.org/10/ggdf97
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