Your search
PROGRAMMING LANGUAGES
Publication year
Results
50 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
-
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
-
Desharnais, J., Edalat, A., & Panangaden, P. (2002). Bisimulation for Labelled Markov Processes. Information and Computation, 179(2), 163–193. https://doi.org/10/fmp9vd
-
Ehrhard, T., & Regnier, L. (2003). The differential lambda-calculus. Theoretical Computer Science, 309(1), 1–41. https://doi.org/10/bf3b8v
-
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
-
Varacca, D., & Winskel, G. (2006). Distributing probability over non-determinism. Mathematical Structures in Computer Science, 16(01), 87. https://doi.org/10/czs9sx
-
Wilkinson, D. J. (2006). Stochastic Modelling for Systems Biology. CRC Press.
-
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
-
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
-
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
-
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
-
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
-
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
-
Manzyuk, O. (2012). A Simply Typed λ-Calculus of Forward Automatic Differentiation. Electronic Notes in Theoretical Computer Science, 286, 257–272. https://doi.org/10/ggdm57
-
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
-
Ghahramani, Z. (2015). Probabilistic machine learning and artificial intelligence. Nature, 521(7553), 452–459. https://doi.org/10/gdxwhq
-
Hur, C.-K., Nori, A. V., & Rajamani, S. K. (2015). A Provably Correct Sampler for Probabilistic Programs, 21.
-
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
-
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
-
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
Explore
BIOLOGY, NEUROSCIENCE & PSYCHOLOGY
- Biology (4)
CATEGORICAL LOGIC
- Effectus theory (1)
- Linear logic (13)
DIFFERENTIAL CALCULUS
- Differentiation (9)
MACHINE LEARNING
- Machine Learning (8)
MODEL CHECKING AND STATE MACHINES
- Coalgebras (2)
- Rewriting theory (5)
- Symbolic logic (3)
- Transition systems (9)
PROBABILITY & STATISTICS
PROGRAMMING LANGUAGES
Methodology
- Implementation (15)
Topic
- Abstract machines (3)
- Algebra (2)
- Automatic differentiation (4)
- Bayesian inference (11)
- Bayesianism (4)
- Biology (4)
- Categorical ML (3)
- Categorical probability theory (3)
- Classical ML (1)
- Coalgebras (2)
- Coherence spaces (4)
- Denotational semantics (17)
- Differential Linear Logic (4)
- Differentiation (9)
- Effectus theory (1)
- Game semantics (3)
- Implementation (15)
- Interactive semantics (4)
- Linear logic (10)
- Machine learning (7)
- Powerdomains (3)
- Probabilistic programming (26)
- Probabilistic transition systems (2)
- Programming language theory (28)
- Rewriting theory (5)
- Semantics (10)
- Symbolic logic (3)
- Systems biology (4)
- Transition systems (4)
Resource type
- Blog Post (1)
- Book (1)
- Book Section (1)
- Computer Program (4)
- Conference Paper (7)
- Journal Article (34)
- Presentation (2)