Bayesian

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Related to Bayesian modeling: Bayesian approach, Bayesian analysis, Bayesian updating

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 ?
Bayesian modeling of multivariate spatial binary data with applications to dental caries.
Their topics include the promontory crannog, the promontory fort, the palisaded enclosure, radiocarbon dating and Bayesian modeling, the material world of Iron Age Wigtownshire, the environment in and around Cults Loch, and liminal living in a dynamic landscape.
We adopt a Bayesian modeling approach due to its inherent advantages (Jaynes 2003):
Uk Kim, "Bayesian modeling of multi-state hierarchical systems with multi-level information aggregation," Reliability Engineering & System Safety, vol.
The first estimate used the same Bayesian modeling workflow used to derive the data-derived prior distribution, including the same prior distributions for the model parameters.
Kuo, "Non-parametric Bayesian modeling of hazard rate with a change point for nanoelectronic devices," IIE Transactions, vol.
JAGS (Just Another Gibbs Sampler) is a program for analysis of Bayesian models using MCMC methods, which was written in C++ by Martyn Plummer with three objectives: (a) to have a cross-platform engine for the BUGS language, (b) to be extensible, allowing users to write their own functions, distributions, and samplers, and (c) to be a platform for experimentation with ideas in Bayesian modeling (see [18]); moreover, it is a free package.
The advantage of our fully Bayesian modeling approach compared to frequentist approaches is that we obtain full inference, not only for the posterior distributions of the regression coefficients but also for the posterior selection probabilities of all the variables.
This work is extended to the Bayesian modeling of the GARCH(1,1) model with Student-t innovations in Ardia and Hoogerheide (2010).
They have provided a unified Bayesian modeling and inference framework under methodology based on Markov chain sampling.
Cluckie, "Fuzzy Bayesian modeling of sea-level along the east coast of Britain," IEEE Transactions on Fuzzy Systems, vol.

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