Bayesian


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Bayes·i·an

 (bā′zē-ən, -zhən)
adj.
Of or relating to an approach to probability in which prior results are used to calculate probabilities of certain present or future events.

[After Thomas Bayes (1702-1761), British cleric and mathematician.]

Bayesian

(ˈbeɪzɪən)
adj
(Statistics) (of a theory) presupposing known a priori probabilities which may be subjectively assessed and which can be revised in the light of experience in accordance with Bayes' theorem. A hypothesis is thus confirmed by an experimental observation which is likely given the hypothesis and unlikely without it. Compare maximum likelihood
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Adj.1.Bayesian - of or relating to statistical methods based on Bayes' theorem
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References in periodicals archive ?
Whereas, the use of the Empirical Bayesian method before the construction of thematic maps, refines the mortality rates, since they have been used in studies of other types of cancer (11,14).
The first is mathematical model-based, such as multinomial logistic regression and bayesian network (BN).
This study was to analyze the association of infections with PPROM in pregnant women in Shaanxi, China, and to establish Bayesian stepwise discriminant analysis to predict the incidence of PPROM.
In this paper we carry out a comparative analysis of Bayesian and Maximum Likelihood (ML) parameter estimates of the All Share Index (ALSI) at the Johannesburg Stock Exchange (JSE).
Other complex pharmacokinetic methods also exist that require dedicated software and use either non-Bayesian least-squares (where the population model is not well-known) or Bayesian least-squares methods (where the population model is reasonably well-known) (2).
Arguing that Bayesian approaches offer distinct and profound advantages in achieving many of the goals of psychometrics, Levy and Mislevy present a Bayesian perspective on psychometrics as an alternative to conventional approaches that is not only viable but in many respects preferable.
ABSTRACT: This study presented an innovative Bayesian Network (BN) modelling and simulation for supplier selection problem of an actual electronic parts manufacturing firm of Pakistan.
Inspired by the multivariate graphical models based on Bayesian networks, which has been shown to be robust and reliable in estimating functional interactions and less sensitive to noise in the fMRI signals [28-30], recently, several Bayesian-inference-based methods were proposed to infer global functional interactions within brain networks and their temporal transition boundaries [31-36].
3) Assessment methods based on modern mathematical theories: the main methods of evaluation are AHP analysis, Bayesian network, Fuzzy Comprehensive Evaluation, Artificial Neural Network method, Grey Comprehensive Evaluation, and Monte Carlo Simulation Method.
To solve this problem and make those measurements incentive compatible, I will introduce a new market institution, called Bayesian markets.
However, the Nakagami distribution has not been considered frequently for the analysis under the Bayesian framework.