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PROGRAMMING LANGUAGES
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7 resources-
Dal Lago, U., & Hoshino, N. (2019). The Geometry of Bayesian Programming (pp. 1–13). https://doi.org/10/ggdk85
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Kerjean, M., & Pacaud Lemay, J.-S. (2019). Higher-Order Distributions for Differential Linear Logic. In M. Bojańczyk & A. Simpson (Eds.), Foundations of Software Science and Computation Structures (pp. 330–347). Cham: Springer International Publishing. https://doi.org/10/ggdmrj
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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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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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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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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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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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