Bayesian theory

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Bayesian theory

[′bāz·ē·ən ‚thē·ə·rē]
(statistics)
A theory, as of statistical inference or decision making, in which probabilities are associated with individual events or statements rather than with sequences of events.
References in periodicals archive ?
The priori information, the posterior information and the likelihood in bayesian probability theory are represented by probability distributions.
These methods include multi-criteria assessment, developing guiding principles ora vision, assessment through Bayesian probability networks, and formative evaluation.
The technical approach is based on the application of the Bayesian probability model.
The local probabilities used in computing the conditional mutual information is computed using the popular Bayesian probability that uses the prior probability of a variable belonging to a natural sentiment class (i.
As cyberthreat analysts, experts rely on the company's machine learning and Bayesian probability theory to protect customers from cyberthreats.
Standard probability distributions, chi-square testing, Bayesian probability, and inferential statistics are then covered.
More specifically, the cornerstone of logical inference is embodied by Bayes' theorem which itself is nothing more than the product law of probability calculus (or 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.
Bayesian probability is an interpretation concept that provides an accurate framework to increase the dependability of decision making systems under uncertainty .
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).
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