Bayesian analysis


Also found in: Encyclopedia.

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 ?
According to the Bayesian analysis at 12 months, success was judged as an 80% or higher probability of achieving a Clinically Significant Difference (CSD: a 25% or greater reduction in the rate of decline in ADCOMS compared to placebo).
Liu (2015), "A Bayesian Analysis of Autoregressive Models with Exogenous Variables and Power-Transformed and Threshold GARCH Errors," Communications in Statistics, Theory and Methods 44: 1967-1980.
On the other hand, advanced analysis methods, including multivariable analysis, Bayesian analysis, longitudinal analysis, and Cox model, were emphasized by more journals compared with ICMJE guidelines.
Furthermore, in Bayesian analysis of epidemiologic data, prior specification could be based on just one expert opinion which may be biased or overconfident.
After the application of Bayesian analysis theoretically, we design a Markov Chain Monte Carlo (MCMC) scheme to sample the posterior with a random initial block indicator because that is the information we do not know ahead and we want to make inference.
Therefore, we have considered the Bayesian analysis of the distribution under different priors and loss functions in order to find the most appropriate combination of loss function and prior for the estimation of the scale parameter of the distribution.
The article "Manage Toward Success" proposes a statistical methodology called Bayesian analysis to orient the enormous amount of acquisition data and evidence to support decision making.
Bayesian analysis of a matched case-control study with expert prior information on both the misclassification of exposure and the exposure-disease association.
Classification includes techniques such as logistic regression, naive Bayesian analysis, decision trees, K-nearest neighbors, and Support Vector Machines.
Gelman, Carlin, Stern, Dunson, Vehtari, and Rubin present this third edition Bayesian analysis text with both introductory and in-depth components.