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33 resources-
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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Castellan, S., Clairambault, P., Paquet, H., & Winskel, G. (2018). The concurrent game semantics of Probabilistic PCF. In Proceedings of the 33rd Annual ACM/IEEE Symposium on Logic in Computer Science - LICS ’18 (pp. 215–224). Oxford, United Kingdom: ACM Press. https://doi.org/10/ggdjfz
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Dal Lago, U., & Hoshino, N. (2019). The Geometry of Bayesian Programming (pp. 1–13). https://doi.org/10/ggdk85
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Danos, V., & Harmer, R. (2000). Probabilistic game semantics (Vol. 3, pp. 204–213). Presented at the ACM Transactions on Computational Logic - TOCL. https://doi.org/10/b6k43s
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Desharnais, J., Edalat, A., & Panangaden, P. (2002). Bisimulation for Labelled Markov Processes. Information and Computation, 179(2), 163–193. https://doi.org/10/fmp9vd
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Ehrhard, T. (2016). An introduction to Differential Linear Logic: proof-nets, models and antiderivatives. ArXiv:1606.01642 [Cs]. Retrieved from http://arxiv.org/abs/1606.01642
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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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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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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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Ehrhard, T., Pagani, M., & Tasson, C. (2011). The Computational Meaning of Probabilistic Coherence Spaces. In 2011 IEEE 26th Annual Symposium on Logic in Computer Science (pp. 87–96). Toronto, ON, Canada: IEEE. https://doi.org/10/cpv52n
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Ehrhard, T., & Regnier, L. (2003). The differential lambda-calculus. Theoretical Computer Science, 309(1), 1–41. https://doi.org/10/bf3b8v
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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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Fages, F., Calzone, L., & Soliman, S. (2006). BIOCHAM: an environment for modeling biological systems and formalizing experimental knowledge. Bioinformatics, 22(14), 1805–1807. https://doi.org/10/dfv
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Fages, F., Martinez, T., Rosenblueth, D. A., & Soliman, S. (2018). Influence Networks Compared with Reaction Networks: Semantics, Expressivity and Attractors. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 15(4), 1138–1151. https://doi.org/10/ggdf94
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Fiore, M., Gambino, N., Hyland, M., & Winskel, G. (2008). The cartesian closed bicategory of generalised species of structures. Journal of the London Mathematical Society, 77(1), 203–220. https://doi.org/10/bd2mr9
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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
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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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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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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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