The paper presents a casual-probabilistic approach to the technical diagnosis in which the solution of the technical diagnostic problem is considered as a probabilistic inference on a special kind of Bayesian networks called Diagnostic Bayesian Networks. A mechanism of probabilistic inference and an algorithm for inference control are described. It is provided that a diagnostic problem represented by a singly connected Diagnostic Bayesian network can be decomposed to a sequence of subproblems with directed tree of multitree topology which are exactly solved in the sense of minimizing the average number of executed tests. The applications of the approach and future trends are briefly discussed
Besides detecting failures and predicting future health conditions of technical systems, fault diagn...
This master's thesis deals with demonstration of various approaches to probabilistic inference in Ba...
Computer-based diagnostic decision support systems (DSS) will play an increasingly important role in...
AbstractThe article presents the main bases of artificial intelligence, probabilistic diagnostic met...
Diagnosis applications relying on Artificial Intelligence methods must deal with uncertain knowledge...
When developing real-world applications of Bayesian networks one of the largest obstacles is the hig...
Several scientific works have modeled medical problems with assistance of Bayesian networks, assisti...
Includes bibliographical references (page 48).San Diego State University copy: the accompanying CD-R...
Bayesian networks have proven their value in solving complex diagnostic problems. The main bottlenec...
One of the most difficult obstacles in the practical application of probabilistic methods is the eff...
The feasibility of diagnostic reasoning in a Bayesian belief network, based on a genetic algorithm i...
Bayesian networks (BN) are a valid method to analyze causal dependencies with uncertainties and to c...
International audienceIn the literature, several fault diagnosis methods, qualitative as well quanti...
The author’s approach generates diagnosis model in the face of uncertainty in the relationship among...
We review recent developments in applying Bayesian probabilistic and statistical ideas to expert sys...
Besides detecting failures and predicting future health conditions of technical systems, fault diagn...
This master's thesis deals with demonstration of various approaches to probabilistic inference in Ba...
Computer-based diagnostic decision support systems (DSS) will play an increasingly important role in...
AbstractThe article presents the main bases of artificial intelligence, probabilistic diagnostic met...
Diagnosis applications relying on Artificial Intelligence methods must deal with uncertain knowledge...
When developing real-world applications of Bayesian networks one of the largest obstacles is the hig...
Several scientific works have modeled medical problems with assistance of Bayesian networks, assisti...
Includes bibliographical references (page 48).San Diego State University copy: the accompanying CD-R...
Bayesian networks have proven their value in solving complex diagnostic problems. The main bottlenec...
One of the most difficult obstacles in the practical application of probabilistic methods is the eff...
The feasibility of diagnostic reasoning in a Bayesian belief network, based on a genetic algorithm i...
Bayesian networks (BN) are a valid method to analyze causal dependencies with uncertainties and to c...
International audienceIn the literature, several fault diagnosis methods, qualitative as well quanti...
The author’s approach generates diagnosis model in the face of uncertainty in the relationship among...
We review recent developments in applying Bayesian probabilistic and statistical ideas to expert sys...
Besides detecting failures and predicting future health conditions of technical systems, fault diagn...
This master's thesis deals with demonstration of various approaches to probabilistic inference in Ba...
Computer-based diagnostic decision support systems (DSS) will play an increasingly important role in...