Probabilistic Expert Systems for Reasoning in Clinical Depressive Disorders

2016 ◽  
Author(s):  
Blessing Ojeme ◽  
Audrey Mbogho ◽  
Thomas Meyer
Keyword(s):  
Expert Systems ◽  
Depressive Disorders ◽  
Probabilistic Expert Systems

scholarly journals A Note on Equivalence Classes of Directed Acyclic Independence Graphs

1993 ◽  
Vol 7 (3) ◽  
pp. 409-412 ◽  
Author(s):  
David Madigan
Keyword(s):  
Model Selection ◽  
Expert Systems ◽  
Equivalence Classes ◽  
Influence Diagrams ◽  
Single Class ◽  
Selection Procedures ◽  
Probabilistic Expert Systems ◽  
Recent Developments ◽  
Independence Graphs ◽  
Markov Properties

Directed acyclic independence graphs (DAIGs) play an important role in recent developments in probabilistic expert systems and influence diagrams (Chyu [1]). The purpose of this note is to show that DAIGs can usefully be grouped into equivalence classes where the members of a single class share identical Markov properties. These equivalence classes can be identified via a simple graphical criterion. This result is particularly relevant to model selection procedures for DAIGs (see, e.g., Cooper and Herskovits [2] and Madigan and Raftery [4]) because it reduces the problem of searching among possible orientations of a given graph to that of searching among the equivalence classes.

Probabilistic expert systems for DNA mixture profiling

2003 ◽  
Vol 63 (3) ◽  
pp. 191-205 ◽  
Author(s):  
J. Mortera ◽  
A.P. Dawid ◽  
S.L. Lauritzen
Keyword(s):  
Expert Systems ◽  
Probabilistic Expert Systems ◽  
Dna Mixture

Analysis of complex family cases with probabilistic expert systems

2009 ◽  
Vol 2 (1) ◽  
pp. 472-474
Author(s):  
Massimo Lancia ◽  
Alessio Coletti ◽  
Marina Dobosz ◽  
Eugenia Carnevali ◽  
Susanna Massetti ◽  
...  
Keyword(s):  
Expert Systems ◽  
Probabilistic Expert Systems

Probabilistic expert systems

1997 ◽  
Vol 5 (3) ◽  
pp. 442-443
Author(s):  
Slawomir T. Wierzchon
Keyword(s):  
Expert Systems ◽  
Probabilistic Expert Systems

Probabilistic expert systems and graphical modelling: a case study in drug safety

1991 ◽  
Vol 337 (1647) ◽  
pp. 387-405 ◽  
Keyword(s):  
Expert Systems ◽  
Case Reports ◽  
Joint Probability ◽  
Biological Knowledge ◽  
Joint Probability Distribution ◽  
Suspected Adverse Reaction ◽  
Probabilistic Expert Systems ◽  
Post Marketing Surveillance ◽  
Graphical Modelling ◽  
Risk Parameters

Probabilistic expert systems are intended to provide reasoned guidance in complex environments characterized by extensive uncertainty . An explicit 'causal' model is constructed for the process being observed, in which an acyclic directed graph is used to express conditional independence assumptions about variables, and probability assessments specify a full joint probability distribution. The resulting graphical structure can cope with a range of issues that arise in realistic modelling. Here we consider a particular example of assessing the chance that a suspected adverse reaction is due to a particular drug under suspicion. The background biological knowledge provides an appropriate model and probability assessments are obtained from expert microbiologists. The model allows a variety of interpretations for ‘causality’. Details of the graphical and computational algorithms used to perform efficient calculations of conditional probabilities on complex graphical structures are provided and illustrated with the example. Further developments should allow updating of the risk parameters in the light of a series of case reports, and may form the basis for a flexible expert system for causality assessment and post-marketing surveillance.

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