The effect of inaccuracies in the parameters of a dynamic Bayesian network can be investigated by subjecting the network to a sensitivity analysis. Having detailed the sensitivity functions involved in our previous work, we now study the effect of parameter inaccuracies on a recommended decision in view of a threshold decisionmaking model. We describe the effect of varying one or more parameter...

Evidence of the strong relationship between learning and emotion has fueled recent work in modeling affective states in intelligent tutoring systems. Many of these models are based on general models of affect without a specific focus on learner emotions. This paper presents work that investigates the benefits of using theoretical models of learner emotions to guide the development of Bayesian n...

Oral cancer is the predominant neoplasm of the head and neck. Annually, more than 500.000 new cases of oral cancer are reported, worldwide. After the initial treatment of cancer and its complete disappearance, a state called remission, reoccurrence rates still remain quite high and the early identification of such relapses is a matter of great importance. Up to now, several approaches have been...

Diagnosis and prediction m some domains, like medical and industrial diagnosis, require a representation that combines uncertainty management and temporal reasoning. Based on the fact that in many cases there are few state changes in the temporal range of interest, we propose a novel represen­ tation called Temporal Nodes Bayesian Network (TNBN). In a TNBN each node represents an event or state...

In this paper we introduce a new dynamic Bayesian network that separates the speakers and their speaking turns in a multi-person conversation. We protect the speakers’ privacy by using only features from which intelligible speech cannot be reconstructed. The model we present combines data from multiple audio streams, segments the streams into speech and silence, separates the different speakers...

We present further developments in our work on using data from real users to build a probabilistic model of user affect based on Dynamic Bayesian Networks (DBNs) and designed to detect multiple emotions. We present analysis and solutions for inaccuracies identified by a previous evaluation; refining the model’s appraisals of events to reflect more closely those of real users. Our findings lead ...

Systems of ordinary differential equations (ODEs) are often used to model the dynamics of complex biological pathways. We construct a discrete state model as a probabilistic approximation of the ODE dynamics by discretizing the value space and the time domain. We then sample a representative set of trajectories and exploit the discretization and the structure of the signaling pathway to encode ...