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 ?
Furthermore, the structure of the ontology defines the relationships between each of the questions and Bayesian probability tables define their relative effect.
As cyberthreat analysts, experts rely on the company's machine learning and Bayesian probability theory to protect customers from cyberthreats.
As cyber threat analysts, they will rely on Darktrace's machine learning and Bayesian probability theory developed by researchers located in Cambridge, England, to protect customers against serious cyber threats.
Standard probability distributions, chi-square testing, Bayesian probability, and inferential statistics are then covered.
He is applying a statistical method known as Bayesian probability theory to translate the calculations that children make during learning tasks into computational models.
The above Bayesian probability theory allows one to model uncertainty about the events and outcomes of interest by combining common-sense knowledge and observational evidence.
By following this identification method, Apgar contends that the risk managers of the firm can use a Bayesian probability framework that takes into account new information or evidence that can support or refute a given hypothesis.
Bayesian probability maps were produced for each sex and age group, but for illustrative purposes we present predicted probability of prevalence >50% in boys ages 13-16 years (the group with the highest infection prevalence; Figure 2).
In many practical applications, parameter estimation for naive Bayes models uses the method of maximum likelihood; in other words, one can work with the naive Bayes model without believing in Bayesian probability or using any Bayesian methods.
Even when the robust investor slants his worst-case probability to put probability I on the long-run risk model, the gap between the ordinary Bayesian probability and this worst-case probability contributes a potential source of time-varying countercyclical risk premia.