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 ?
The priori information, the posterior information and the likelihood in bayesian probability theory are represented by probability distributions.
As cyberthreat analysts, experts rely on the company's machine learning and Bayesian probability theory to protect customers from cyberthreats.
Although Bayesian probability theory offers a coherent and rational approach for source reconstruction, its application to real-world problems using real sensor networks and operational dispersion models will require a better understanding of both the scale and structure of the model error in the predicted concentrations.
While possibility theory and the associated fuzzy set theory are of interest to the control world, the process described in this paper is more suited to the framework of Bayesian probability theory.
Bayesian probability theory requires us to make our best guess about the future and then continually revise it as we get new information.
He is applying a statistical method known as Bayesian probability theory to translate the calculations that children make during learning tasks into computational models.
After explaining the basic principles of Bayesian probability theory, the book illustrates their use with a range of examples.