Uncertainty in Artificial Intelligence
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A Causal Bayesian Model for the Diagnosis of Appendicitis
Stanley Schwartz, Jonathan Baron, John Clarke
The causal Bayesian approach is based on the assumption that effects (e.g., symptoms) that are not conditionally independent with respect to some causal agent (e.g., a disease) are conditionally independent with respect to some intermediate state caused by the agent, (e.g., a pathological condition). This paper describes the development of a causal Bayesian model for the diagnosis of appendicitis. The paper begins with a description of the standard Bayesian approach to reasoning about uncertainty and the major critiques it faces. The paper then lays the theoretical groundwork for the causal extension of the Bayesian approach, and details specific improvements we have developed. The paper then goes on to describe our knowledge engineering and implementation and the results of a test of the system. The paper concludes with a discussion of how the causal Bayesian approach deals with the criticisms of the standard Bayesian model and why it is superior to alternative approaches to reasoning about uncertainty popular in the Al community.
Keywords: Casual Bayesian Approach
Pages: 229-236
PS Link:
PDF Link: /papers/86/p229-schwartz.pdf
AUTHOR = "Stanley Schwartz and Jonathan Baron and John Clarke",
TITLE = "A Causal Bayesian Model for the Diagnosis of Appendicitis",
BOOKTITLE = "Proceedings of the Second Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-86)",
ADDRESS = "Corvallis, Oregon",
YEAR = "1986",
PAGES = "229--236"

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