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PROGRAMMING LANGUAGES
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35 resources-
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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Abramsky, S., Haghverdi, E., & Scott, P. (2002). Geometry of Interaction and Linear Combinatory Algebras. Mathematical. Structures in Comp. Sci., 12(5), 625–665. https://doi.org/10/fcsmhm
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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., & Regnier, L. (2003). The differential lambda-calculus. Theoretical Computer Science, 309(1), 1–41. https://doi.org/10/bf3b8v
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van Breugel, F., Mislove, M., Ouaknine, J., & Worrell, J. (2005). Domain theory, testing and simulation for labelled Markov processes. Theoretical Computer Science, 333(1), 171–197. https://doi.org/10/ft9vc5
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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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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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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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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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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., 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., & 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., 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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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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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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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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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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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. (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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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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