Bayesian analysis

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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 ?
Bayesian inference structure gives the posterior probability of the entire function, and serves as the reference for parameter optimization.
On this basis, we introduced the exception detection algorithm for web service composition by Bayesian inference.
Bayesian inference overcomes many of these limitations, providing a flexible, coherent method for determining savings and saving uncertainty for a wide range of linear and nonlinear problems encountered in retrofit projects.
Dr McCulloch does not cover this possibility, or emphasise the reliance of Bayesian inference on this value.
It is worth noting that there are multiple foundational problems that bedevil anthropic reasoning (such as how to define an "observer" (5)) and even if these are overcome, there are good reasons to doubt that the probabilities needed to carry out the Bayesian inferences can ever be made accurate enough to be of value to science.
Bayesian inference on major loci in related multigeneration selection lines of laying hens.
See Appendix B for a list of other textbooks and articles that provide introductions to Bayesian inference in the social sciences that focus on the use of R and WinBUGS.
Based on Autonomy's unique Bayesian inference technology, Auminence represents a breakthrough for healthcare providers seeking to transform healthcare delivery through a deeper and more meaningful understanding of their data.
The seven methods to be investigated include areal weighting (1); two variations of network length, one with roads weighted by type (2), and the second using residential roads only (3); two variations of surface image interpolation, one with land covers weighted by type (4), and the second using low and medium intensity land cover types only (5); and two statistical inference methods using road length and area to predict population, one a linear regression model (6), and the second a Bayesian inference estimation model (7).
Bayesian Inference for the Mixed Conditional Heteroskedasticity model, Econometrics Journal.
It uses a program called the Bayesian Inference Engine running unobtrusively on a user's computer to monitor the person's click patterns and so to determine how they respond to different textual and visual cues.
The authors first provide an overview of the principles of GP and machine learning techniques and then discuss their integration, which rests on tailoring GP's tree-structured representation to the data in the form of a polynomial neural network, using the ensemble technique to improve the classification performance of GP, and using the probabilistic learning technique to enhance the program's evolution through the integration of GP with the Bayesian inference method and the grammatical inference approach.

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