License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.CALCO.2017.25
URN: urn:nbn:de:0030-drops-80529
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Cho, Kenta ; Jacobs, Bart

The EfProb Library for Probabilistic Calculations

LIPIcs-CALCO-2017-25.pdf (0.5 MB)


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.

BibTeX - Entry

  author =	{Kenta Cho and Bart Jacobs},
  title =	{{The EfProb Library for Probabilistic Calculations}},
  booktitle =	{7th Conference on Algebra and Coalgebra in Computer Science (CALCO 2017)},
  pages =	{25:1--25:8},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-033-0},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{72},
  editor =	{Filippo Bonchi and Barbara K{\"o}nig},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{},
  URN =		{urn:nbn:de:0030-drops-80529},
  doi =		{10.4230/LIPIcs.CALCO.2017.25},
  annote =	{Keywords:  probability, embedded language, effectus theory}

Keywords: probability, embedded language, effectus theory
Collection: 7th Conference on Algebra and Coalgebra in Computer Science (CALCO 2017)
Issue Date: 2017
Date of publication: 17.11.2017

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