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Baudart, G., Mandel, L., Atkinson, E., Sherman, B., Pouzet, M., & Carbin, M. (2019). Reactive Probabilistic Programming. ArXiv:1908.07563 [Cs]. Retrieved from http://arxiv.org/abs/1908.07563

Dal Lago, U., & Hoshino, N. (2019). The Geometry of Bayesian Programming (pp. 1–13). https://doi.org/10/ggdk85

Vákár, M., Kammar, O., & Staton, S. (2018). A Domain Theory for Statistical Probabilistic Programming. ArXiv:1811.04196 [Cs]. Retrieved from http://arxiv.org/abs/1811.04196

Ścibior, A., Kammar, O., Vákár, M., Staton, S., Yang, H., Cai, Y., … Ghahramani, Z. (2017). Denotational validation of higherorder Bayesian inference. Proceedings of the ACM on Programming Languages, 2(POPL), 1–29. https://doi.org/10.1145/3158148

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
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