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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

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

Jacobs, B. (2018). Categorical Aspects of Parameter Learning. ArXiv:1810.05814 [Cs]. Retrieved from http://arxiv.org/abs/1810.05814

Jacobs, B., & Zanasi, F. (2018). The Logical Essentials of Bayesian Reasoning. ArXiv:1804.01193 [Cs]. Retrieved from http://arxiv.org/abs/1804.01193

Clerc, F., Danos, V., Dahlqvist, F., & Garnier, I. (2017). Pointless learning (long version). Retrieved from https://hal.archivesouvertes.fr/hal01429663

Staton, S. (2017). Commutative Semantics for Probabilistic Programming. In H. Yang (Ed.), Programming Languages and Systems (Vol. 10201, pp. 855–879). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/9783662544341_32

Jacobs, B., & Zanasi, F. (2017). A Formal Semantics of Influence in Bayesian Reasoning. Schloss Dagstuhl  LeibnizZentrum Fuer Informatik GmbH, Wadern/Saarbruecken, Germany. https://doi.org/10/ggdgbc

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

Staton, S., Yang, H., Heunen, C., Kammar, O., & Wood, F. (2016). Semantics for probabilistic programming: higherorder 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

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

Culbertson, J., & Sturtz, K. (2013). Bayesian machine learning via category theory. ArXiv:1312.1445 [Math]. Retrieved from http://arxiv.org/abs/1312.1445

Watanabe, S. (2009, August). Algebraic Geometry and Statistical Learning Theory. https://doi.org/10.1017/CBO9780511800474

McCullagh, P. (2002). What is a statistical model? The Annals of Statistics, 30(5), 1225–1310. https://doi.org/10/bkts3m

Wermuth, N., & Cox, D. R. (2001). Graphical Models: Overview. In N. J. Smelser & P. B. Baltes (Eds.), International Encyclopedia of the Social & Behavioral Sciences (pp. 6379–6386). Oxford: Pergamon. https://doi.org/10.1016/B0080430767/00440X

Heckerman, D. (1995). A Tutorial on Learning With Bayesian Networks. Retrieved from https://www.microsoft.com/enus/research/publication/atutorialonlearningwithbayesiannetworks/
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