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Statistical Inference, Bayesian
Bayesian statistical inference is based on the subjective interpretation of probability. Uncertainty about an unknown quantity or a parameter of interest is expressed by a probability distribution that represents the agent’s subjective degrees of belief. Bayesian Conditionalization and Bayes’s Theorem describe how learning data changes this (prior) probability distribution into a posterior probability distribution. Standard procedures of statistical inference, such as point and interval estimation, can be formulated as functions of the posterior distribution. Hypothesis tests are evaluated by means of the Bayes factor—that is, the degree to which the odds for either hypothesis have changed in the light of the data. All in all, Bayesian statistical inference has high flexibility and can be applied to a large number of important statistical problems, such as ...
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