Add to ORKGDiagnostic reasoning in essence amounts to reasoning about an unobservable condition, based on indirect observations from diagnostic tests. Probabilistic networks that are de-veloped for diagnostic reasoning, typically take the reliability characteristics of the tests employed into consideration to avoid misdiagnosis. In this paper, we demonstrate the ef-fects of inaccuracies in these characteristics by means of a sensitivity analysis of a real-life network in the medical domain. Key words: probabilistic networks, reliability characteristics, sensitivity analysis 1
The paper presents a casual-probabilistic approach to the technical diagnosis in which the solution ...
Objective: One of the hardest technical tasks in employing Bayesian network models in practice is ob...
This paper relates our experience in developing a mechanism for reasoning about the differential dia...
A probabilistic network built for an application domain often has a single output variable of intere...
AbstractBayesian belief networks are being increasingly used as a knowledge representation for reaso...
Sensitivity analysis is a general technique for investigating the robustness of the output of a math...
Diagnosis applications relying on Artificial Intelligence methods must deal with uncertain knowledge...
When building a Bayesian belief network, usually a large number of probabilities have to be assessed...
The assessments for the various conditional probabilities of a Bayesian belief network inevitably ar...
Sensitivity analysis of network reliability using Monte Carlo (revision 1, Dec. 17, 2005) We analyze...
Probabilistic networks are now fairly well established as practical representations of knowl-edge fo...
Contains fulltext : 36354.pdf (author's version ) (Closed access
The dissertation explores the problem of rigorously quantifying the performance of a fault diagnosis...
ments for the degree of Doctor of Science. A probabilistic graph is a linear graph in which both nod...
In the past decades Machine Learning tools have been successfully used in several medical diagnostic...
The paper presents a casual-probabilistic approach to the technical diagnosis in which the solution ...
Objective: One of the hardest technical tasks in employing Bayesian network models in practice is ob...
This paper relates our experience in developing a mechanism for reasoning about the differential dia...
A probabilistic network built for an application domain often has a single output variable of intere...
AbstractBayesian belief networks are being increasingly used as a knowledge representation for reaso...
Sensitivity analysis is a general technique for investigating the robustness of the output of a math...
Diagnosis applications relying on Artificial Intelligence methods must deal with uncertain knowledge...
When building a Bayesian belief network, usually a large number of probabilities have to be assessed...
The assessments for the various conditional probabilities of a Bayesian belief network inevitably ar...
Sensitivity analysis of network reliability using Monte Carlo (revision 1, Dec. 17, 2005) We analyze...
Probabilistic networks are now fairly well established as practical representations of knowl-edge fo...
Contains fulltext : 36354.pdf (author's version ) (Closed access
The dissertation explores the problem of rigorously quantifying the performance of a fault diagnosis...
ments for the degree of Doctor of Science. A probabilistic graph is a linear graph in which both nod...
In the past decades Machine Learning tools have been successfully used in several medical diagnostic...
The paper presents a casual-probabilistic approach to the technical diagnosis in which the solution ...
Objective: One of the hardest technical tasks in employing Bayesian network models in practice is ob...
This paper relates our experience in developing a mechanism for reasoning about the differential dia...