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6 resources-
Ścibior, A., Kammar, O., Vákár, M., Staton, S., Yang, H., Cai, Y., … Ghahramani, Z. (2017). Denotational validation of higher-order Bayesian inference.
*Proceedings of the ACM on Programming Languages*,*2*(POPL), 1–29. https://doi.org/10.1145/3158148 -
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 -
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 -
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 -
Varacca, D., & Winskel, G. (2006). Distributing probability over non-determinism.
*Mathematical Structures in Computer Science*,*16*(01), 87. https://doi.org/10/czs9sx -
de Vink, E. P., & Rutten, J. J. M. M. (1997). Bisimulation for probabilistic transition systems: A coalgebraic approach. In P. Degano, R. Gorrieri, & A. Marchetti-Spaccamela (Eds.),
*Automata, Languages and Programming*(pp. 460–470). Berlin, Heidelberg: Springer. https://doi.org/10/fcqzmk

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