The ALARM Monitoring System: A Case Study with two Probabilistic Inference Techniques for Belief Networks
Lecture notes in medical informaticsPublished 1 January 1989
Ingo A. Beinlich, Henri J. Suermondt, R. Martin Chavez, Gregory F. Cooper
Citations741
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
Two algorithms were applied to this belief network: a message-passing algorithm by Pearl for probability updating in multiply connected networks using the method of conditioning and the Lauritzen-Spiegelhalter algorithm for local probability computations on graphical structures.
Abstract
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.
Keywords
Computer Science
Journal of the Royal Statistical Society Series B (Statistical Methodology)Local Computations with Probabilities on Graphical Structures and Their Application to Expert Systems
3,970 Citations1988Steffen L. Lauritzen, David J. Spiegelhalter
This work exploits a range of local representations for the joint probability distribution, combined with topological changes to the original network termed 'marrying' and 'filling-in', which allows efficient algorithms for transfer between representations, providing rapid absorption and propagation of evidence.
Artificial IntelligenceFusion, propagation, and structuring in belief networks
2,152 Citations1986Judea Pearl
Elsevier eBooksUncertainty in Artificial Intelligence
1,434 Citations1992
Qualitative Probabilistic Reasoning and Cognitive models, Dempster-Shafer Theory in Knowledge Representation, and Possibility Theory: Semantics and Applications.
Operations ResearchEvaluating Influence Diagrams
1,276 Citations1986Ross D. Shachter
An algorithm is developed that can evaluate any well-formed influence diagram and determine the optimal policy for its decisions and can be performed using the decision maker's perspective on the problem.
SIAM Journal on ComputingSimple Linear-Time Algorithms to Test Chordality of Graphs, Test Acyclicity of Hypergraphs, and Selectively Reduce Acyclic Hypergraphs
1,060 Citations1984Robert E. Tarjan, Mihalis Yannakakis
An article of golfing equipment has a golf tee attached to a spring-biassed reel by a length of string which can be aligned with the green or hole and used as an aid in swinging the club face in the correct direction.
SIAM Journal on ComputingAlgorithms for Minimum Coloring, Maximum Clique, Minimum Covering by Cliques, and Maximum Independent Set of a Chordal Graph
530 Citations1972Fǎnicǎ Gavril
This paper presents ways for constructing efficient algorithms for finding a minimum coloring, a minimum covering by cliques, a maximum clique, and a maximum independent set given a chordal graph.
The Annals of StatisticsMarkov Fields and Log-Linear Interaction Models for Contingency Tables
437 Citations1980J. N. Darroch, Steffen L. Lauritzen +1 more
A close connection between the theory of Markov fields and that of log-linear interaction models for contingency tables is used to define and investigate a new class of models for such tables, graphical models, which are hierarchical models that can be represented by a simple, undirected graph.
Research Portal (King's College London)MUNIN: a causal probabilistic network for interpretation of electromyographic findings
205 Citations1987Steen Andreassen, Marianne Woldbye +2 more
Experience gained through building a causal network for interpretation of electromyographic findings has shown that probabilistic inference is a realistic possibility in networks of non-trivial size.
Artificial IntelligenceDistributed revision of composite beliefs
190 Citations1987Judea Pearl
The chapter demonstrates how numerical probabilities can be used as a catalyst for coherent transformations from evidence sentences to conclusion sentences and identifies the structures that allow such transformations to be executed by autonomous production rules and those that invite problems of intractability and instability unless treated with care.
Machine intelligence and pattern recognitionA Constraint – Propagation Approach to Probabilistic Reasoning
169 Citations1986Judea Pearl
The paper demonstrates that strict adherence to probability theory does not preclude the use of concurrent, self-activated constraint-propagation mechanisms for managing uncertainty and maintains local records of sources-of-belief.
AI MagazineThinking Backward for Knowledge Acquisition
64 Citations1987Ross D. Shachter, David Heckerman
It is argued that a knowledge base be constructed following the expert’s natural reasoning direction, and then reverse the direction for use to facilitate knowledge acquisition in deterministic domains and is essential when a problem involves uncertainty.
Journal of the Royal Statistical Society Series D (The Statistician)Coherent Evidence Propagation in Expert Systems
17 Citations1987David J. Spiegelhalter
It is shown how coherent probabilistic reasoning is made theoretically straightforward by utilising recent work relating probability models to directed and undirected graphs, and how published algorithms allow efficient machine implementation.
arXiv (Cornell University)Updating Probabilities in Multiply-Connected Belief Networks
13 Citations2013Jaap Suermondt, Gregory F. Cooper
This paper presents a heuristic algorithm for finding a loop-cutset that satisfies conditions that need to be satisfied by the selected nodes of a multiply-connected belief networks.
Machine intelligence and pattern recognitionKNET: Integrating Hypermedia and Normative Bayesian Modeling
13 Citations1990R. Martin Chavez, Gregory F. Cooper
KNET is a general-purpose shell for constructing expert systems based on belief networks and decision networks that features a coherent probabilistic scheme for managing uncertainty, an object-oriented graphics editor for drawing and manipulating decision networks, and HyperCard's potential for quickly constructing flexible and friendly user interfaces.