Bayesian Probability

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Bayesian Probability

A revision of a previous probability based on new information. In Bayesian analysis, one makes mathematical assumptions about unavailable information. As that information is gathered and disseminated, the Bayesian probability corrects or replaces the assumptions and alters its results accordingly.
References in periodicals archive ?
On statistical front, the self-driving cars use Baysian probabilities to recognize patterns and then make decisions.
This problem can be overcome by employing the Akaike information criterion (AIC) and Schwartz Baysian Criteria (SBC).
Authors Banks, Rios, and Insua present students, academics, researchers, and professionals working in a wide variety of contexts with an examination of the construction of Baysian models for the strategic analysis of rival or oppositional forces.
Two approaches, ordinary and Baysian LUT-based skin detection are evaluated.
Nie et al [8] proposed to apply DPM [17] to detect moving people in each frame and utilized Baysian model to handle tracklet association problem, which can address occlusion problem in tracking process.
This study also suggested the Baysian method using a Gibbs sampler for testing epistatic effects among limited number (up to 4) of loci.
Equation (2) in the form of TAR mad M-TAR has been estimated with lag-lengths of 2 and 4 based on the Akaike Information criterion (AIC) and the Schwarz Baysian criterion (SBC).
Achcar, JA, de Cassia Favoretti, A and Mazucheli, J, "Approximate Baysian analysis for the Michaelis-Menten model", Journal of the Chilean Statistical Society 1996; 13: 1-23.
In each test the lag length was determined by the Baysian information criterion.
Baysian decision theory has traditionally been used to predict customer choice (Kotler, 1988), but it does not separate the probability of choosing within the consideration set from choosing a combination of sets.