The key problem of inductive-learning in Bayes network is the estimator of prior distribution. This paper adopted general native Bayes to handle continuous variables, proposed a kind of kernel function constructed by orthogonal polynomials, which is used to estimate the density function of prior distribution in Bayes network. Paper then made further researches into optimality of kernel density ...

In this paper, the well-known proportional hazards model which includes several well-known lifetime distributions such as exponential, Pareto, Lomax, Burr type XII, and so on is considered. With both Bayesian and non-Bayesian approaches , we consider the estimation of parameters of interest based on progressively Type-II right censored samples. The Bayes estimates are obtained based on symmetri...

Bayesian predictions are stochastic just like predictions of any other inference scheme that generalize from a nite sample. While a simple variational argument shows that Bayes averaging is generalization optimal given that the prior matches the teacher parameter distribution the situation is less clear if the teacher distribution is unknown. I deene a class of averaging procedures, the tempera...

The first term is the estimation error and measures the performance of the discrimination rule with respect to the best hypothesis in H. In previous lectures, we studied performance guarantees for this quantity when ĥn is ERM. Now, we also consider the approximation error, which captures how well a class of hypotheses {Hk} approximates the Bayes decision boundary. For example, consider the hist...