Bayesian logic

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Bayesian logic

Reasoning (logic) in which the likelihood of an event occurring can be described in quantitative or probabilistic terms

Bayesian logic

A type of reasoning in which the likelihood of an event occurring can be described in quantitative—ie probabilistic terms. See Artificial intelligence, Computer-assisted diagnosis.
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Key Words: invasive species, economic valuation, Burmese python, Bayesian probability
After explaining the basic principles of Bayesian probability theory, the book illustrates their use with a range of examples.
This formalizes the notion so common in philosophy of science and Bayesian probability theory today of bootstrapping to the best explanation.
Indeed, maximum likelihood and Bayesian probability theory offer the correct formalism for considering all data and model uncertainties; least-squares analysis is just one, albeit relatively general, instance of maximum likelihood.
Here I have been, toiling in the fields of Evidenceland for some years, laboring along with others to show how use of Bayesian probability theory can assist in the analysis and understanding of evidentiary problems.
Practitioners and laboratories can devise better methods for monitoring comprehension of and compliance with the various components within the 11 steps of TTP, practitioners can use information contained in the serum drug concentration more expansively and effectively than is typical, laboratories can improve the application and reporting of serum drug concentrations and interpretation, and educators can teach students and practitioners the theory and application of Bayesian probability revision, test performance characteristics, and economic analyses necessary to make more effective use of TDM.
Statistical experts can focus on building powerful QSAR models leveraging Bayesian probability, recursive partitioning, neural networks, linear regression and other native QSAR methods.
Bayesian probability is an interpretation concept that provides an accurate framework to increase the dependability of decision making systems under uncertainty [5].
The Tabasco 2003 isolate grouped with 3 strains from 2001 and 2002 in Florida and Louisiana and more distantly with a New York isolate from 2000, with strong Bayesian probability and bootstrap support; inclusion of the New York grouse strain was weakly supported (bootstrap and Bayesian probability values <80%).