Technical Keynote Address.- Computational Intelligence for Bioinformatics: The Knowledge Engineering Approach.- Best Technical paper.- Reusing JessTab Rules in Protégé.- Information Learning, Integration and Management.- Robot Docking Based on Omnidirectional Vision and Reinforcement Learning.- Global EM Learning of Finite Mixture Models using the Greedy Elimination Method.- Tracking Drifting Concepts by Time Window Optimisation.- Hierarchical knowledge-oriented specification for information integration.- Al and the World Wide Web.- The Semantic Web as a Linguistic Resource: Opportunities for Natural Language Generation.- A Distributed CBR Framework through Semantic Web Services.- Using simple ontologies to build personal Webs of knowledge.- Modeling Navigation Patterns of Visitors of Unstructured Websites.- Networks and Biologically Motivated Al.- Exploring the Noisy Threshold Function in Designing Bayesian Networks.- A biologically motivated neural network architecture for the avoidance of catastrophic interference.- Fast Estimation of Distribution Algorithm (EDA) via Constrained Multi-Parent Recombination.- Multi-Agent Systems.- A Trading Agent for a Multi-Issue Clearing House.- An Agent-Based Approach to ANN Training.- Case-Based Reasoning.- Collaborative Recommending using Formal Concept Analysis.- Using Case Differences for Regression in CBR Systems.- Formal Concept Analysis for Knowledge Refinement in Case Based Reasoning.- Recommendation Knowledge Discovery.- Knowledge Discovery in Data.- Improved Methods for Extracting Frequent Itemsets from Interim-Support Trees.- The Impact of Rule Ranking on the Quality of Associative Classifiers.- Reliable Instance Classification with Version Spaces.- Reasoning and Decision Making.- Acting Irrationally to ImprovePerformance in Stochastic Worlds.- On the Use of OBDDs in Model Based Diagnosis: an Approach Based on the Partition of the Model.- Issues in Designing Tutors for Games of Incomplete Information: a Bridge Case Study.- Qualitative Representation and Reasoning with Uncertainty in Space and Time.
"SESSION 2: NETWORKS AND BIOLOGICALLV MOTIVATED AI (S. 132-133)
Exploring the Noisy Threshold Function in Designing Bayesian Networks*
Rasa Jurgelenaite, Peter Lucas and Tom Heskes Radboud University Nijmegen, Nijmegen, The Netherlands E-mail : {rasa.peterl.tomh}@cs.ru.nl
Abstract
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. Many Bayesian network models incorporate causal independence assumptions; however, only the noisy OR and noisy AND, two examples of causal independence models, are used in practice. Their underlying assumption that either at least one cause, or all causes together, give rise to an effect, however, seems unnecessarily restrictive. In the present paper a new, more flexible, causal independence model is proposed, based on the Boolean threshold function. A connection is established between conditional probability distributions based on the noisy threshold model and Poisson binomial distributions, and the basic properties of this probability distribution are studied in some depth. The successful application of the noisy threshold model in the refinement of a Bayesian network for the diagnosis and treatment of ventilator-associated pneumonia demonstrates the practical value of the presented theory.
1 Introduction
Bayesian networks offer an appealing language for building models of domains with inherent uncertainty. However, the assessment of a probability distribution in Bayesian networks is a challenging task, even if its topology is sparse. This task becomes even more complex if the model has to integrate expert knowledge. While learning algorithms can be forced to take into account an experts view, for the best possible results the experts must be willing to reconsider their ideas in light of the models discovered structure.
This requires a clear understanding of the model by the domain expert. Causal independence models can both limit the number of conditional probabilities to be assessed and provide the ability for models to be understood by domain experts in the field. The concept of causal independence refers to a situation where multiple causes independently influence a common effect. Many actual Bayesian network models use causal independence assumptions . However, only the logical OR and AND operators are used in practice in defining the interaction among causes; their underlying assumption is that the presence of either at least one cause or all causes at the same time give"
