In many categorical approaches to measure theory and probability, one considers a category of spaces, such as measurable spaces or topological spaces, and equips this category with a monad whose functor part assigns to each space $X$ a space $P X$ of measures, probability measures, or valuations on $X$, or a variation thereof.
For probability theory, this can be interpreted as adding to the points of a space $X$ new “random points”, modelled as probability measures or valuations. The old points, which we can think of as deterministic, are embedded in $P X$ via the unit of the monad $X\to P X$. Just as well, the Kleisli morphisms of $P$ can be seen as stochastic maps?. (Monads can be seen as ways of extending our spaces and functions to account for new phenomena, see for example extension system and monad in computer science.) Note that these probability measures are technically different from random elements?: they rather correspond to the laws? of the random elements.
Algebras of probability and measure monads can be interpreted as generalized convex spaces or conical spaces of a certain kind. For probability theory, in particular, the algebras of a probability monad can be seen as spaces equipped with a notion of expectation value of a random variable.
The details vary depending on the monad and on the category under consideration.
Many choices of categories and of monads are possible, depending on which aspects of measure theory or probability one wants to study. See the table below for more details.
The term “probability monad” was coined by Giry herself (see here).
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W. Lawvere, The category of probabilistic mappings, ms. 12 pages, 1962
Michèle Giry, A categorical approach to probability theory, Categorical aspects of topology and analysis (Ottawa, Ont., 1980), pp. 68–85, Lecture Notes in Math. 915 Springer 1982.
T. Swirszcz, Monadic functors and convexity, Bulletin de l’Academie Polonais des Sciences 22, 1974 (pdf)
Klaus Keimel?, The monad of probability measures over compact ordered spaces and its Eilenberg-Moore algebras, Topology and its Applications, 2008 (doi:10.1016/j.topol.2008.07.002)
Reinhold Heckmann, Spaces of valuations, Papers on General Topology and Ap-plications, 1996 (doi:10.1111/j.1749-6632.1996.tb49168.x,pdf)
Mauricio Alvarez-Manilla, Achin Jung, Klaus Keimel?, The probabilistic powerdomain for stably compact spaces, Theoretical Computer Science 328, 2004. Link here.
C. Jones and Gordon. D. Plotkin?, A probabilistic powerdomain of evaluations, LICS 4, 1989. (doi:10.1109/LICS.1989.39173)
Jean Goubault-Larrecq? and Xiaodong Jia, Algebras of the extended probabilistic powerdomain monad, ENTCS 345, 2019
Tobias Fritz, Paolo Perrone and Sharwin Rezagholi, Probability, valuations, hyperspace: Three monads on Top and the support as a morphism, 2019. Link here.
Steve Vickers, A monad of valuation locales, 2011. Link here.
Franck van Breugel, The metric monad for probabilistic nondeterminism, unpublished, 2005. (pdf)
Tobias Fritz and Paolo Perrone, A probability monad as the colimit of spaces of finite samples, Theory and Applications of Categories 34, 2019. (pdf)
Tobias Fritz and Paolo Perrone, Stochastic order on metric spaces and the ordered Kantorovich monad, submitted, 2018. (arXiv:1808.09898)
Bart Jacobs, From probability monads to commutative effectuses, Journal of Logical and Algebraic Methods in Programming 94, 2018.
Tobias Fritz, Convex spaces I: definitions and examples, 2009. (arXiv:0903.5522)
Last revised on November 17, 2019 at 22:03:08. See the history of this page for a list of all contributions to it.