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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
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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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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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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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Ścibior, A., Kammar, O., & Ghahramani, Z. (2018). Functional programming for modular Bayesian inference. Proceedings of the ACM on Programming Languages, 2(ICFP), 1–29. https://doi.org/10/gft39x
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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., 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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Tran, D., Hoffman, M. D., Saurous, R. A., Brevdo, E., Murphy, K., & Blei, D. M. (2017). Deep Probabilistic Programming. ArXiv:1701.03757 [Cs, Stat]. Retrieved from http://arxiv.org/abs/1701.03757
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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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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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Staton, S., Yang, H., Heunen, C., Kammar, O., & Wood, F. (2016). Semantics for probabilistic programming: higher-order 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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Hur, C.-K., Nori, A. V., & Rajamani, S. K. (2015). A Provably Correct Sampler for Probabilistic Programs, 21.
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Ghahramani, Z. (2015). Probabilistic machine learning and artificial intelligence. Nature, 521(7553), 452–459. https://doi.org/10/gdxwhq
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Ehrhard, T., & Danos, V. (2011). Probabilistic coherence spaces as a model of higher-order probabilistic computation. Information and Computation, 209(6), 966–991. https://doi.org/10/ctfch6
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Tix, R., Keimel, K., & Plotkin, G. (2009). Semantic Domains for Combining Probability and Non-Determinism. Electronic Notes in Theoretical Computer Science, 222, 3–99. https://doi.org/10/d9hwq7
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Jones, C. (1989). Probabilistic Non-determinism, 198.
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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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