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32 resources-
Wilkinson, D. (2019). A compositional approach to scalable Bayesian computation and probabilistic programming.
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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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Borgström, J., Lago, U. D., Gordon, A. D., & Szymczak, M. (2017). A Lambda-Calculus Foundation for Universal Probabilistic Programming. ArXiv:1512.08990 [Cs]. Retrieved from http://arxiv.org/abs/1512.08990
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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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Jones, C., & Plotkin, G. (1989). A Probabilistic Powerdomain of Evaluations. In Proceedings of the Fourth Annual Symposium on Logic in Computer Science (pp. 186–195). Piscataway, NJ, USA: IEEE Press. Retrieved from http://dl.acm.org/citation.cfm?id=77350.77370
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Hur, C.-K., Nori, A. V., & Rajamani, S. K. (2015). A Provably Correct Sampler for Probabilistic Programs, 21.
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Manzyuk, O. (2012). A Simply Typed λ-Calculus of Forward Automatic Differentiation. Electronic Notes in Theoretical Computer Science, 286, 257–272. https://doi.org/10/ggdm57
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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/10.1007/978-3-662-54434-1_32
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Paquet, H., & Winskel, G. (2018). Continuous Probability Distributions in Concurrent Games. Electronic Notes in Theoretical Computer Science, 341, 321–344. https://doi.org/10/ggdmwv
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Kammar, O., Staton, S., & Vákár, M. (2018). Diffeological Spaces and Denotational Semantics for Differential Programming.
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Ehrhard, T. (2019). Differentials and distances in probabilistic coherence spaces. ArXiv:1902.04836 [Cs]. Retrieved from http://arxiv.org/abs/1902.04836
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Varacca, D., & Winskel, G. (2006). Distributing probability over non-determinism. Mathematical Structures in Computer Science, 16(01), 87. https://doi.org/10/czs9sx
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Law, J., & Wilkinson, D. (2019). Functional probabilistic programming for scalable Bayesian modelling. ArXiv:1908.02062 [Stat]. Retrieved from http://arxiv.org/abs/1908.02062
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Plotkin, G. D. (1977). LCF considered as a programming language. Theoretical Computer Science, 5(3), 223–255. https://doi.org/10/dc7fdn
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Ehrhard, T., Pagani, M., & Tasson, C. (2017). Measurable Cones and Stable, Measurable Functions. Proceedings of the ACM on Programming Languages, 2(POPL), 1–28. https://doi.org/10/ggdjf8
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Keimel, K., & Plotkin, G. D. (2017). Mixed powerdomains for probability and nondeterminism. ArXiv:1612.01005 [Cs]. https://doi.org/10/ggdmrp
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Ścibior, A., Ghahramani, Z., & Gordon, A. D. (2015). Practical Probabilistic Programming with Monads. In Proceedings of the 2015 ACM SIGPLAN Symposium on Haskell (pp. 165–176). New York, NY, USA: ACM. https://doi.org/10/gft39z
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Keimel, K., & Plotkin, G. d. (2009). Predicate Transformers for Extended Probability and Non-determinism. Mathematical. Structures in Comp. Sci., 19(3), 501–539. https://doi.org/10/bkvgqc
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Ehrhard, T., & Tasson, C. (2018). Probabilistic call by push value. ArXiv:1607.04690 [Cs]. https://doi.org/10/ggdk8z
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Ehrhard, T., Tasson, C., & Pagani, M. (2014). Probabilistic coherence spaces are fully abstract for probabilistic PCF. In Proceedings of the 41st ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages - POPL ’14 (pp. 309–320). San Diego, California, USA: ACM Press. https://doi.org/10/ggdf9x
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