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'''Bayesian probability''' is a particular calculus of inductive plausible reasoning based on the works of [[Jacob Bernoulli|Bernoulli]], [[Rev Thomas Bayes|Bayes]], [[Pierre Simon de Laplace|Laplace]] (and more recently of [[Sir Harold Jeffreys|Jeffreys]], [[Richard T. Cox|Cox]], and [[Edwin T. Jaynes|Jaynes]]) in which probability is interpreted as the degree that a proposition/hypothesis/model is true ranging from complete certainty to complete certainty of its falsehood and all intermediate values.

'''Bayesian probability''' is a particular calculus of inductive plausible reasoning based on the works of [[Jacob Bernoulli|Bernoulli]], [[Rev Thomas Bayes|Bayes]], [[Pierre Simon de Laplace|Laplace]] (and more recently of [[Sir Harold Jeffreys|Jeffreys]], [[Richard T. Cox|Cox]], and [[Edwin T. Jaynes|Jaynes]]) in which probability is interpreted as the degree that a proposition/hypothesis/model is true ranging from complete certainty to complete certainty of its falsehood and all intermediate values.  Unlike frequentist statistics, Bayesian statistics starts from initial "estimates" of probabilities, which may reflect anecdotal evidence and/or ideology.  Probabilities are revised as evidence is accumulated.

[[category:Probability and Statistics]]

[[category:Probability and Statistics]]