In part I of this two-part study, we introduced a new optimal Bayesian classification methodology that utilizes the same modeling framework proposed in Bayesian minimum-mean-square error (MMSE) error estimation. Optimal Bayesian classification thus completes a Bayesian theory of classification, where both the classifier error and our estimate of the error may be simultaneously optimized and stu...

Learning a Bayesian network structure from data is an NP-hard problem and thus exact algorithms are feasible only for small data sets. Therefore, network structures for larger networks are usually learned with various heuristics. Another approach to scaling up the structure learning is local learning. In local learning, the modeler has one or more target variables that are of special interest; ...

We study Bayesian discriminative inference given a model family p(c,x, θ) that is assumed to contain all our prior information but still known to be incorrect. This falls in between “standard” Bayesian generative modeling and Bayesian regression, where the margin p(x, θ) is known to be uninformative about p(c|x, θ). We give an axiomatic proof that discriminative posterior is consistent for cond...

We study Bayesian discriminative inference given a model family p(c,x, θ) that is assumed to contain all our prior information but still known to be incorrect. This falls in between “standard” Bayesian generative modeling and Bayesian regression, where the margin p(x, θ) is known to be uninformative about p(c|x, θ). We give an axiomatic proof that discriminative posterior is consistent for cond...

This paper proposes a model and an architecture for designing intelligent tutoring system using Bayesian Networks. The design model of an intelligent tutoring system is directed towards the separation between the domain knowledge and the tutor shell. The architecture is composed by a user model, a knowledge base, an adaptation module, a pedagogical module and a presentation module. Bayesian Net...

In this paper we propose a user model that aims to assess the user's exploratory behaviour in an open environment. The model is based on a Bayesian Network and consists of several components that allow diagnosis of the causes of poor exploratory behaviour. Among these components are the user's knowledge of exploration strategies, the user's motivation level, personality traits and emotional sta...