Bayesian Probability

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Bayesian Probability

A revision of a previous probability based on new information. In Bayesian analysis, one makes mathematical assumptions about unavailable information. As that information is gathered and disseminated, the Bayesian probability corrects or replaces the assumptions and alters its results accordingly.
References in periodicals archive ?
This classification algorithm predicts the possibility of a class relation pattern when the prior probability and conditional probability are known, which is based on Bayesian theory of probability statistics.
A modest knowledge of probability and statistics is required, they say, in particular readers should know the basic concepts of maximum likelihood estimation and Bayesian theory.
It discusses this analysis using two-dimensional semantics and the Bayesian theory.
The section 'Judgement and Decision-Making' highlights the disagreements among the contributors on whether the proper framework for human cognition should be Bayesian theory, heuristics, or naive statistics, and it considers the assessment of the normative implications of these alternatives.
Clark includes a description of Bayesian theory for comparison and contrast and a number of exercises readers can use to uncover the nature of the models using both classic and new data sets.
Interestingly, Chapline [13] noted that neither Bayesian theory nor Dempster-Shafer could offer insight on how to minimize overall energy usage in the network.
Essentially, Bayesian theory tells us how to express and manipulate probabilities.
Models of model uncertainty that have the decisionmakers entertain multiple models require modifying the Bayesian theory of learning.