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PROBABILITY & STATISTICS

## Results

10 resources-
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 -
Ścibior, A., Kammar, O., Vákár, M., Staton, S., Yang, H., Cai, Y., … Ghahramani, Z. (2017). Denotational validation of higher-order Bayesian inference.
*Proceedings of the ACM on Programming Languages*,*2*(POPL), 1–29. https://doi.org/10.1145/3158148 -
Clerc, F., Danos, V., Dahlqvist, F., & Garnier, I. (2017).
*Pointless learning (long version)*. Retrieved from https://hal.archives-ouvertes.fr/hal-01429663 -
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 -
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 -
Culbertson, J., & Sturtz, K. (2013). Bayesian machine learning via category theory.
*ArXiv:1312.1445 [Math]*. Retrieved from http://arxiv.org/abs/1312.1445 -
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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