A munin network for the median nerve-a case study on loops
Applied Artificial Intelligence
Communication complexity
A factorized representation of independence of causal influence and lazy propagation
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
A Tractable Inference Algorithm for Diagnosing Multiple Diseases
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Exploiting causal independence in Bayesian network inference
Journal of Artificial Intelligence Research
Multiplicative factorization of noisy-max
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Lazy propagation in junction trees
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Bayesian networks in educational testing
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems - New trends in probabilistic graphical models
Predicting carcinoid heart disease with the noisy-threshold classifier
Artificial Intelligence in Medicine
Triangulation Heuristics for BN2O Networks
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Marginalization without summation: exploiting determinism in factor algebra
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Artificial Intelligence in Medicine
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In this paper we propose an efficient method for Bayesian network inference in models with functional dependence. We generalize the multiplicative factorization method originally designed by Takikawa and D'Ambrosio (1999) for models with independence of causal influence. Using a hidden variable, we transform a probability potential into a product of two-dimensional potentials. The multiplicative factorization yields more efficient inference. For example, in junction tree propagation it helps to avoid large cliques. In order to keep potentials small, the number of states of the hidden variable should be minimized. We transform this problem into a combinatorial problem of minimal base in a particular space. We present an example of a computerized adaptive test, in which the factorization method is significantly more efficient than previous inference methods.