Bayesian analysis of binary sequences

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.