We study the problem of learning Bayesian network structures from data. We develop an algorithm for finding the k-best Bayesian network structures. We propose to compute the posterior probabilities of hypotheses of interest by Bayesian model averaging over the k-best Bayesian networks. We present empirical results on structural discovery over several real and synthetic data sets and show that t...

A current popular approach to representing time in Bayesian belief networks is through Dynamic Bayesian Networks (DBNs) (Dean & Kanazawa 1989). DBNs connect sequences of entire Bayes networks, each representing a situation at a snapshot in time. We present an alternative method for incorporating time into Bayesian belief networks that utilizes abstractions of temporal representation. This metho...

In this paper, we introduce a new method to improve the performance of combining boosting and naïve Bayesian. Instead of combining boosting and Naïve Bayesian learning directly, which was proved to be unsatisfactory to improve performance, we select the training samples dynamically by bootstrap method for the construction of naïve Bayesian classifiers, and hence generate very different or unsta...

Psychologists have been trained to do data analysis by asking whether null values can be rejected. Is the difference between groups nonzero? Is choice accuracy not at chance level? These questions have been traditionally addressed by null hypothesis significance testing (NHST). NHST has deep problems that are solved by Bayesian data analysis. As psychologists transition to Bayesian data analysi...

= Abstract = The accuracy of Bayesian feedback method for predicting phenytoin dosage was assessed and compared to conventional predictive methods such as the Richens & Dunlop method and Ludden method from 5 7 patients who took phenytoin for the treatment of grandma1 epilepsy. For the comparison of absolute predictability of each method, correlation coefficient between predicted and actual dosa...