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 ?
Hobbs [8] uses Bayesian probabilities to examine the risk of climate change on water resources, but does not extend this to drinking water quality or quantity.
0 (Wolfram 1996), that can be used for estimating parameters and test the different hypotheses as well as estimating the Bayesian probabilities of the different ecological scenarios from a given data set, can be downloaded from the World Wide Web.
The estimated Bayesian probabilities should not be regarded as probabilities of different ecological hypothesis being true or not true (Edwards 1992), but rather as an alternative description of the data that focus on the dynamics of plant communities.
Standard Bayesian probabilities are based on the assumption that the supremum of the betting-quotients which give an advantage to the bettor-on is equal to the infimum of the quotients which give an advantage to the bettor-against.
These algorithms, based on Claude Shannon's principles of information theory, Bayesian probabilities, and the latest research in neural networks, became the foundation for an approach called Adaptive Probabilistic Concept Modeling (APCM).