Bayesian analysis

(redirected from Bayesian inference)
Also found in: Financial, Encyclopedia, Wikipedia.

Bayesian analysis

A decision analysis which permits the calculation of the probability that one treatment is superior to another based on the observed data and prior beliefs. In Bayesian analysis, subjectivity is not a liability, but rather explicitly allows different opinions to be formally expressed and evaluated.

Bayesian analysis

A decision-making analysis that '…permits the calculation of the probability that one treatment is superior based on the observed data and prior beliefs…subjectivity of beliefs is not a liability, but rather explicitly allows different opinions to be formally expressed and evaluated.' See Algorithm, Critical pathway, Decision analysis.

Bayes the·o·rem

(bāyz thē'ŏr-ĕm)
A method of calculating statistical probability that combines a prior estimate of probability with statistics derived from subsequent events or experiments. Although it lacks mathematical rigor, it is often used to infer degree of risk in various medical settings.
Synonym(s): bayesian analysis.
References in periodicals archive ?
In order to build a causal network among factors in NAS, it is important to incorporate Bayesian inference in data-mining process.
Bayesian inferences permit to calculate the joint posterior probability of the different variables which can overcome the limitations of fault tree regarding the diagnosis.
Kelly and Smith (2011) presented the process of Bayesian Inference as:
Based on the rough salient region, we transform the computation of saliency into a probability inference problem, using the Bayesian inference, for estimating the posterior probability at each pixel v of the image:
Bayesian inference provides an unproven, but potentially powerful, alternative approach to quantify climate model uncertainties from individual models.
Bayesian inference procedures have been particularly important.
For the same real data, Kim and Han [5] applied a Bayesian inference method based on the conjugate prior of the scale parameter of the Rayleigh distribution under general progressive censoring and S.
In this paper, we are aiming to pinpoint the function dynamic problems, including detecting magnitude change points, functional connectivity change points, and functional interaction patters, which have been addressed using Bayesian inference.
Lately, from the sparse Bayesian learning (SBL) perspective, several approaches are proposed, such as off-grid sparse Bayesian inference (OGSBI) [20], sparse adaptive calibration recovery via iterative maximum a posteriori (SACR-iMAP) [6], and block SBL (BSBL) [21].

Full browser ?