Many collaborative filtering enabled Web sites that recommend books, CDs, movies, videos and so on, have become very popular on Internet. They recommend items to a user based on the opinions of other users with similar tastes. In this paper, we discuss an approach to collaborative filtering based on the simple Bayesian classifier. The simple Bayesian classifier is one of the most successful sup...

The label switching problem is one of the fundamental problems in Bayesian mixture analysis. Using all the Markov chain Monte Carlo samples as the initials for the EM algorithm, we propose to label the samples based on the modes they converge to. Our method is based on the assumption that the samples converged to the same mode have the same labels. If a relative noninformative prior is used or ...

In the usual Bayesian approach to survey sampling the sampling design, plays a minimal role, at best. Although a close relationship between exchangeable prior distributions and simple random sampling has been noted; how to formally integrate simple random sampling into the Bayesian paradigm is not clear. Recently it has been argued that the sampling design can be thought of as part of a Bayesia...

It is well known that a social choice function is truthfully implementable in Bayesian Nash equilibrium if and only if it is incentive compatible. However, in general it is not possible to rule out other equilibrium outcomes, and additional conditions, e.g., Bayesian monotonicity, are needed to ensure full implementation. We argue that this multiplicity problem is not very severe for virtual Ba...

This paper discusses the use of various scoring metrics in the Bayesian optimization algorithm BOA which uses Bayesian networks to model promising solutions and generate the new ones The use of decision graphs in Bayesian networks to improve the performance of the BOA is proposed To favor simple models a complexity measure is incorporated into the Bayesian Dirichlet metric for Bayesian networks...