# 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.
McGraw-Hill Dictionary of Scientific & Technical Terms, 6E, Copyright © 2003 by The McGraw-Hill Companies, Inc.
References in periodicals archive ?
In the proposed algorithm, we model the CTU partition process as a multi-class classification problem and solve it using Bayesian theory. Fig.
According to Bayesian theory, the point estimations of the posterior distribution of the two parameters are 7021.3 and 1.1640.
The Bayesian theory has proven to be efficient to solve fractional inverse problems (see [12, 13]).
Bayesian method uses the prior knowledge and can realize the accurate computation, and it is more and more used in the research of gene locus mining, for example, using Bayesian theory to mine the disease associated loci , identifying pig nipple number related genes , detecting gene loci associated with breast cancer , and detecting the interaction between specific phenotypic traits loci (Zhang et al.
Based on the Bayesian theory, the probability of the error recognition is 
In the Bayesian theory, we know that posterior probability [varies] likelihood x prior probability.
Hawboldt, "Dynamic risk assessment using failure assessment and Bayesian theory," Journal of Loss Prevention in the Process Industries, vol.
In this book, Bayesian theory is discussed from chapter 6 to chapter 9.
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.
The discussion of the use of the Bayesian theory continues well into the final section that centers on more existential problems: climate change, terrorism, and the bubbles in financial markets.
Tsokos, Bayesian Theory and Methods, Springer, 2014.
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.
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