1,571 research outputs found
A Tractable Inference Algorithm for Diagnosing Multiple Diseases
We examine a probabilistic model for the diagnosis of multiple diseases. In
the model, diseases and findings are represented as binary variables. Also,
diseases are marginally independent, features are conditionally independent
given disease instances, and diseases interact to produce findings via a noisy
OR-gate. An algorithm for computing the posterior probability of each disease,
given a set of observed findings, called quickscore, is presented. The time
complexity of the algorithm is O(nm-2m+), where n is the number of diseases, m+
is the number of positive findings and m- is the number of negative findings.
Although the time complexity of quickscore i5 exponential in the number of
positive findings, the algorithm is useful in practice because the number of
observed positive findings is usually far less than the number of diseases
under consideration. Performance results for quickscore applied to a
probabilistic version of Quick Medical Reference (QMR) are provided.Comment: Appears in Proceedings of the Fifth Conference on Uncertainty in
Artificial Intelligence (UAI1989
An Empirical Comparison of Three Inference Methods
In this paper, an empirical evaluation of three inference methods for
uncertain reasoning is presented in the context of Pathfinder, a large expert
system for the diagnosis of lymph-node pathology. The inference procedures
evaluated are (1) Bayes' theorem, assuming evidence is conditionally
independent given each hypothesis; (2) odds-likelihood updating, assuming
evidence is conditionally independent given each hypothesis and given the
negation of each hypothesis; and (3) a inference method related to the
Dempster-Shafer theory of belief. Both expert-rating and decision-theoretic
metrics are used to compare the diagnostic accuracy of the inference methods.Comment: Appears in Proceedings of the Fourth Conference on Uncertainty in
Artificial Intelligence (UAI1988
Decision-Theoretic Foundations for Causal Reasoning
We present a definition of cause and effect in terms of decision-theoretic
primitives and thereby provide a principled foundation for causal reasoning.
Our definition departs from the traditional view of causation in that causal
assertions may vary with the set of decisions available. We argue that this
approach provides added clarity to the notion of cause. Also in this paper, we
examine the encoding of causal relationships in directed acyclic graphs. We
describe a special class of influence diagrams, those in canonical form, and
show its relationship to Pearl's representation of cause and effect. Finally,
we show how canonical form facilitates counterfactual reasoning.Comment: See http://www.jair.org/ for any accompanying file
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