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The EfProb Library for Probabilistic Calculations

Authors Kenta Cho, Bart Jacobs

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Keywords
  • probability
  • embedded language
  • effectus theory

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Abstract

EfProb is an abbreviation of Effectus Probability. It is the name of
a library for probability calculations in Python. EfProb offers a
uniform language for discrete, continuous and quantum probability.
For each of these three cases, the basic ingredients of the language
are states, predicates, and channels. Probabilities are typically
calculated as validities of predicates in states. States can be
updated (conditioned) with predicates. Channels can be used for state
transformation and for predicate transformation. This short paper
gives an overview of the use of EfProb.

Cite As Get BibTex

Kenta Cho and Bart Jacobs. The EfProb Library for Probabilistic Calculations. In 7th Conference on Algebra and Coalgebra in Computer Science (CALCO 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 72, pp. 25:1-25:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017) https://doi.org/10.4230/LIPIcs.CALCO.2017.25

Author Details

Kenta Cho
Bart Jacobs

References

  1. K. Cho, B. Jacobs, A. Westerbaan, and B. Westerbaan. An introduction to effectus theory. Preprint, 2015. arXiv:1512.05813 [cs.LO]. Google Scholar
  2. B. Cobb and P. Shenoy. Inference in hybrid Bayesian networks with mixtures of truncated exponentials. Int. J. Approx. Reasoning, 41(3):257-286, 2006. Google Scholar
  3. N. Goodman, V. Mansinghka, D. Roy, K. Bonawitz, and J. Tenenbaum. Church: a language for generative models. In Uncertainty in Artificial Intelligence, 2008. Google Scholar
  4. A. Green., P. LeF. Lumsdaine, N. Ross, P. Selinger, and B. Valiron. Quipper: A scalable quantum programming language. In Programming Language Design and Implementation, 2013. Google Scholar
  5. B. Jacobs. New directions in categorical logic, for classical, probabilistic and quantum logic. Logical Methods in Comp. Sci., 11(3):1-76, 2015. URL: http://dx.doi.org/10.2168/LMCS-11(3:24)2015.
  6. B. Jacobs and K. Cho. EfProb user manual. 2017. URL: https://efprob.cs.ru.nl.
  7. B. Jacobs and F. Zanasi. A predicate/state transformer semantics for Bayesian learning. In L. Birkedal, editor, Math. Found. of Programming Semantics, number 325 in Elect. Notes in Theor. Comp. Sci., pages 185-200. Elsevier, Amsterdam, 2016. Google Scholar
  8. B. Milch, B. Marthi, S. Russell, D. Sontag, D. Ong, and A. Kolobov. BLOG: Probabilistic models with unknown objects. In L. Getoor and B. Taskar, editors, Introduction to Statistical Relational Learning. MIT Press, 2007. Google Scholar
  9. M. Nielsen and I. Chuang. Quantum Computation and Quantum Information. Cambridge Univ. Press, 2000. Google Scholar
  10. J. Paykin, R. Rand, and S. Zdancewic. QWIRE: A core language for quantum circuits. In Princ. of Programming Languages, pages 846-858. ACM Press, 2017. Google Scholar
  11. F. Wood, J. W. van de Meent, and V. Mansinghka. A new approach to probabilistic programming inference. In Artificial Intelligence and Statistics, 2014. Google Scholar
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