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States of Convex Sets

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 9034)

Abstract

State spaces in probabilistic and quantum computation are convex sets, that is, Eilenberg–Moore algebras of the distribution monad. This article studies some computationally relevant properties of convex sets. We introduce the term effectus for a category with suitable coproducts (so that predicates, as arrows of the shape X → 1 + 1, form effect modules, and states, arrows of the shape 1 → X, form convex sets). One main result is that the category of cancellative convex sets is such an effectus. A second result says that the state functor is a “map of effecti”. We also define ‘normalisation of states’ and show how this property is closed related to conditional probability. This is elaborated in an example of probabilistic Bayesian inference.

Keywords

  • Convex Subset
  • Convex Combination
  • Convex Space
  • Real Vector Space
  • Conditional State

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Jacobs, B., Westerbaan, B., Westerbaan, B. (2015). States of Convex Sets. In: Pitts, A. (eds) Foundations of Software Science and Computation Structures. FoSSaCS 2015. Lecture Notes in Computer Science(), vol 9034. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46678-0_6

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  • DOI: https://doi.org/10.1007/978-3-662-46678-0_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-46677-3

  • Online ISBN: 978-3-662-46678-0

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