This paper concerns the assessment of linear cause-effect relationships from a combination of observational data and qualitative causal structures. The paper shows how techniques developed for identifying causal effects in causal Bayesian networks can be used to identify linear causal effects, and thus provides a new approach for assessing linear causal effects in structural equation models. Us...

Virtual Campusis a research project which aims to provide a comprehensive and innovative e-learning environment for authoring, fruition and evaluation. The paper presents the Tutoring and Validation module developed for Virtual Campus. The Validation tool tracks the learners’ behaviour within the Virtual Campus environment and defines the “user model” in terms of learning attitudes (derived by ...

We discuss issues that arise when applying techniques for the learning of Bayesian networks in the user modeling context. We address the problem of sparse data that is often present in user modeling and show how we try to cope with it by introducing available a-priori knowledge into the learning procedures. Particularly, we present initial results concerning the learning of the structural part ...

[1] A Bayesian nonlinear regression modeling method is introduced and compared with the least squares method for modeling nutrient loads in stream networks. The objective of the study is to better model spatial correlation in river basin hydrology and land use for improving the model as a forecasting tool. The Bayesian modeling approach is introduced in three steps, each with a more complicated...

A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. Dynamic Bayesian networks are an extension of Bayesian networks for modeling dynamic processes. In this paper we present a decision support system based on a dynamic Bayesian network. Its purpose is to monitor the dry weight of patients suffering from chronic renal failure treated by he...

We study a Bayesian framework for density modeling with mixture of exponential family distributions. Our contributions: •A variational Bayesian solution for finite mixture models • Show that finite mixture models (with a Bayesian setting) can determine the mixture number automatically • Justify this result with connections to Dirichlet Process mixture models •A fast variational Bayesian solutio...