Bayesian Statistics

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

Bayesian Statistics

A statistical system using probabilities based on assumptions about unavailable information. As that information is gathered and disseminated, one corrects or replaces the assumptions and the statistical analysis alters its results accordingly.
References in periodicals archive ?
Flight on-time performance, flight delays, national airspace system, business analytics, data mining, decision trees, Bayesian inference
4), and conditional probabilities tables "CPTs" for their child node are given according to the gate types), the bayesian inference can then be conducted.
In this paper, we show that the two-stage Bayesian inference approach may also be applied to combine information from multiple, diverse condition monitoring systems.
The rest of the paper compares two methods for applying Bayesian inference in estimation of the value of information; the first uses numerical integration and the second the Metropolis-Hastings algorithm.
In addition, we believe that the saliency detection could be regarded as a kind of probability problem, which can be resolved by the Bayesian inference.
Enhanced education in Bayesian inference principles and increased awareness of cognitive biases are important goals for healthcare professionals, but is there a better representation of the statistical structure of risk to facilitate rapid Bayesian inference and informed decision-making?
Once we have carried out the Bayesian inference process to obtain posterior distributions for the frequency of OR events and the severity of losses in the previous section, we now proceed to integrate both distributions through Monte Carlo (8) simulation by using the "Compound" function of @Risk.
16) Spiegelhalter D, Thomas A, Best N, WINBUGS: Bayesian Inference Using Gibs Sampling.
The program included a mixture of lectures on fundamental concepts in Bayesian inference and sampling, applications, and hands-on computer laboratory exercises employing software packages for Bayesian inference, Markov chain Monte Carlo methods, and global sensitivity analyses.
The "computation power" referenced in the previous sentence usually means one or more commonly applied algorithms that can greatly help with predictions, such as Bayesian inference, linear regression, and so on.
Full browser ?