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A F M SMITH
Degrees of Belief as Probabilities
A Simple Illustration of Bayesian Learning
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adaptive analysis applied approach approximately assessment assumed assumption average Bayes Bayesian Bayesian methods beliefs calculate calibration close combining compared Computer conditional consider corresponding curve defined denote described discussion distribution effect equal estimate example experts failure faults Figure fixed function further future gamma give given independent indicate individual inferences interest interval judgements known less likelihood Littlewood marginal mathematical maximum mean measure median observed obtained occurs parameters particular performance plots Poisson possible posterior density practice prediction system prior prior beliefs probability problem procedure random variables reasonable refer reliability growth Report represented rule sample sequence shown shows simple situation Smith software reliability specified stage Statistical successive suggests Suppose Table theorem true u-plot uncertainty University unknown usually values weights