The ALARM Monitoring System: A Case Study with two Probabilistic Inference Techniques for Belief Networks

ALARM (A Logical Alarm Reduction Mechanism) is a diagnostic application used to explore probabilistic reasoning techniques in belief networks. ALARM implements an alarm message system for patient monitoring; it calculates probabilities for a differential diagnosis based on available evidence. The medical knowledge is encoded in a graphical structure connecting 8 diagnoses, 16 findings and 13 intermediate variables. Two algorithms were applied to this belief network: (1) a message-passing algorithm by Pearl for probability updating in multiply connected networks using the method of conditioning; and (2) the Lauritzen-Spiegelhalter algorithm for local probability computations on graphical structures. The characteristics of both algorithms are analyzed and their specific applications and time complexities are shown.

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