The modeling of critical infrastructure interdependencies is a challenging task. This paper discusses several interdependency modeling requirements and proposes a Bayesian network approach for modeling interdependencies and inoperability propagation. The approach is applied to a case study involving the Japanese critical infrastructure sectors. Survey data published by the National Institute of...

As Bayesian networks are applied to larger and more complex problem domains, search for exible modeling and more eecient inference methods is an ongoing eeort. Multiply sectioned Bayesian networks (MSBNs) extend the HUGIN inference for Bayesian networks into a coherent framework for exible modeling and distributed inference. Lazy propagation extends the Shafer-Shenoy and HUGIN inference methods...

Mechanistic models of ruminant digestion and metabolism have advanced our understanding of the processes underlying ruminant animal physiology. Deterministic modeling practices ignore the inherent variation within and among individual animals and thus have no way to assess how sources of error influence model outputs. We introduce Bayesian calibration of mathematical models to address the need ...

The object-oriented paradigma is a new but proven technology for modelling mechatronics, i.e. multidisciplinary modelling. For many reasons the object-oriented approach is very much desirable also for qualitative models in system design, diagnosis or verification. Bayesian networks are a very robust technology for qualitative probabilistic modelling. In this paper we present a first approach in...

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We present a framework for affective user modeling that deals with the high level of uncertainty involved in recognizing a variety of user emotions by relying un Dynamic Bayesian Network. We summarize how we used this framework to build a model of player affect to be used by a socially intelligent agent during the interaction with an educational game.

Bayesian Nonparametric Modeling and Theory for Complex Data