We propose a robust estimation method of gene networks based on microarray gene expression data. It is well-known that microarray data contain a large amount of noise and some outl...
Seiya Imoto, Tomoyuki Higuchi, SunYong Kim, Euna J...
Causal independence modelling is a well-known method both for reducing the size of probability tables and for explaining the underlying mechanisms in Bayesian networks. In this pap...
Abstract. There is currently a large interest in relational probabilistic models. While the concept of context-specific independence (CSI) has been well-studied for models such as ...
Multiagent probabilistic reasoning with multiply sectioned Bayesian networks requires interfacing agent subnets (the modeling task) subject to a set of conditions. To specify the ...
An integrated Belief-Desire-Intention (BDI) modeling framework is proposed for human decision making and planning, whose sub-modules are based on Bayesian belief network (BBN), De...