Bayesian analysis of binary sequences

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS

卷:175

Article

Torney, DC

关键词: concave;    convex;    cut polytope;    geometric probability;    Laplace approximation;    machine learning;    moments;    nonlinear optimization;    polytope;    posterior likelihoods;    probability monomials;    quadratic program;    semidefinite;   

DOI  :  10.1016/j.cam.2004.05.010

来源: Elsevier

【 摘 要 】

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.. (C) 2004 Elsevier B.V. All rights reserved.

【 授权许可】

Free