# Your search

PROBABILITY & STATISTICS

## Results

19 resources-
McCullagh, P. (2002). What is a statistical model?
*The Annals of Statistics*,*30*(5), 1225–1310. https://doi.org/10/bkts3m -
Jacobs, B., & Furber, R. (2015). Towards a Categorical Account of Conditional Probability.
*Electronic Proceedings in Theoretical Computer Science*,*195*, 179–195. https://doi.org/10/ggdf9w -
Jacobs, B., & Zanasi, F. (2018). The Logical Essentials of Bayesian Reasoning.
*ArXiv:1804.01193 [Cs]*. Retrieved from http://arxiv.org/abs/1804.01193 -
Jacobs, B. (2017). Quantum effect logic in cognition.
*Journal of Mathematical Psychology*,*81*, 1–10. https://doi.org/10/gcnkcj -
Clerc, F., Danos, V., Dahlqvist, F., & Garnier, I. (2017).
*Pointless learning (long version)*. Retrieved from https://hal.archives-ouvertes.fr/hal-01429663 -
Jacobs, B. (2018). From probability monads to commutative effectuses.
*Journal of Logical and Algebraic Methods in Programming*,*94*, 200–237. https://doi.org/10/gct2wr -
Varacca, D., & Winskel, G. (2006). Distributing probability over non-determinism.
*Mathematical Structures in Computer Science*,*16*(01), 87. https://doi.org/10/czs9sx -
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 -
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 -
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. (2018). Categorical Aspects of Parameter Learning.
*ArXiv:1810.05814 [Cs]*. Retrieved from http://arxiv.org/abs/1810.05814 -
de Vink, E. P., & Rutten, J. J. M. M. (1997). Bisimulation for probabilistic transition systems: A coalgebraic approach. In P. Degano, R. Gorrieri, & A. Marchetti-Spaccamela (Eds.),
*Automata, Languages and Programming*(pp. 460–470). Berlin, Heidelberg: Springer. https://doi.org/10/fcqzmk -
Culbertson, J., & Sturtz, K. (2013). Bayesian machine learning via category theory.
*ArXiv:1312.1445 [Math]*. Retrieved from http://arxiv.org/abs/1312.1445 -
Cho, K., Jacobs, B., Westerbaan, B., & Westerbaan, A. (2015). An Introduction to Effectus Theory.
*ArXiv:1512.05813 [Quant-Ph]*. Retrieved from http://arxiv.org/abs/1512.05813 -
Fritz, T., & Perrone, P. (2019). A Probability Monad as the Colimit of Spaces of Finite Samples.
*ArXiv:1712.05363 [Cs, Math]*. Retrieved from http://arxiv.org/abs/1712.05363 -
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 -
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 -
Heunen, C., Kammar, O., Staton, S., & Yang, H. (2017). A Convenient Category for Higher-Order Probability Theory.
*ArXiv:1701.02547 [Cs, Math]*. Retrieved from http://arxiv.org/abs/1701.02547 -
Giry, M. (1982). A categorical approach to probability theory. In B. Banaschewski (Ed.),
*Categorical Aspects of Topology and Analysis*(pp. 68–85). Berlin, Heidelberg: Springer. https://doi.org/10/dtx5t5

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