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Everything about Regular conditional probability
Concept in probability theory
In probability theory, regular conditional probability is a concept that formalizes the notion of conditioning on the outcome of a random variable. The resulting conditional probability distribution is a parametrized family of probability measures called a Markov kernel. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Regular conditional probabilityA 5-minute orientation
- Skim a recent paper: Universal Regular Conditional Distributions via Probabilistic TransformersInfluential work since 2022
- Pick up a classic book: American FilmThe most-reprinted book on the subject
- Read the landmark paper: On regular conditional probabilitiesCited 23 times
- Go sideways: Disintegration theoremA closely related topic
- Go sideways: Conditional expectationA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
On regular conditional probabilities
Regular conditional probability, disintegration of probability and Radon spaces
Existence of Independent Complements in Regular Conditional Probability Spaces
Perfect Probability Measures and Regular Conditional Probabilities
🆕 Influential since 2022
📚 Books worth reading
American FilmAmerican Film Institute · 1975
The Drunkard's WalkLeonard Mlodinow · 2008
Mathematical structures for computer scienceJudith L. Gersting · 1982
Cartesian meditationsEdmund Husserl, Dorion Cairns · 1960
Ten epochs of church historyFulton, John, 1834-1907, Locke, Clinton, 1829-1904 · 1879
Saint Clare of AssisiErnest Gilliat-Smith · 1914
An Introduction to Probability Theory and Its Applications [2/2]William Feller · 1966
Treaty Occupationay ToxicologySuelen Queiroz · 2010
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
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