Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
9509610 | Journal of Computational and Applied Mathematics | 2005 | 13 Pages |
Abstract
This manuscript details Bayesian methodology for “learning by example”, with binary n-sequences encoding the objects under consideration. Priors prove influential; conformable priors are described. Laplace approximation of Bayes integrals yields posterior likelihoods for all n-sequences. This involves the optimization of a definite function over a convex domain-efficiently effectuated by the sequential application of the quadratic program.
Keywords
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
David C. Torney,