This paper investigates the issue of degeneracy in student modeling with Dynamic Bayesian Network in Prime Climb, an intelligent educational game for practicing number factorization. We discuss that maximizing the common measure of predictive accuracy (i.e. end accuracy) of the student model may not necessarily ensure trusted assessment of learning in the student and that, it could result in im...

This paper is about helping people who make critical decisions improve the quality of their judgements. We provide a brief introduction to Bayesian Nets (BNs) and use an example in safety assessment. We show how BNs enable decision-makers to combine different types of evidence (including subjective judgements) to provide quantitative, auditable arguments. By using state-ofthe-art BN technology ...

Situational awareness can be a valuable indicator of the performance of flight crews and the way pilots manage navigation information can be relevant to its estimation. In this research, dynamic Bayesian networks are applied to a dataset of variables both collected in real time during simulated flights and added with expert knowledge. This paper compares different approaches to the discretizati...

of the Dissertation Acoustically-Driven Talking Face Animations Using Dynamic Bayesian Networks

It is challenging to recognize facial action unit (AU) from spontaneous facial displays, especially when they are accompanied by speech. The major reason is that the information is extracted from a single source, i.e., the visual channel, in the current practice. However, facial activity is highly correlated with voice in natural human communications. Instead of solely improving visual observat...

In the context of learning Bayesian networks from data, very little work has been published on methods for assessing the quality of an induced model. This issue, however, has received a great deal of attention in the statistics literature. In this paper, we take a well-known method from statistics, Efron’s Bootstrap, and examine its applicability for assessing a confidence measure on features o...

In recent years, the growing interest toward complex critical infrastructures and their interdependencies have solicited new efforts in the area of modeling and analysis of large interdependent systems. Cascading effects are a typical phenomenon of dependencies of components inside a system or among systems. The present paper deals with the modeling of cascading effects in a power grid. In part...

Bayesian networks have been successfully applied to the assessment of user properties which remain unchanged during a session. However, many properties of a person vary over time, thus raising new questions of network modeling. In this paperwe characterize different types of dependencies that occur in networks that deal with the modeling of temporally variable user properties. We show how exist...

We present a speech modeling methodology where no a priori assumption is made on the dependencies between the observed and the hidden speech processes. Rather, dependencies are learned form data. This methodology guarantees improvement in modeling fidelity compared to HMMs. In addition, it gives the user a control on the trade-off between modeling accuracy and model complexity. Furthermore, the...

Mathematical statistics uses two major paradigms, conventional (or frequentist), and Bayesian. Bayesian methods provide a complete paradigm for both statistical inference and decision making under uncertainty. Bayesian methods may be derived from an axiomatic system, and hence provide a general, coherent methodology. Bayesian methods contain as particular cases many of the more often used frequ...