The aim of the paper is to formally relate logical Horn models and Bayesian Networks (BNs) in the framework of diagnostic reasoning. This is pursued by pointing out similarities between the two formalisms at the modeling level and by introducing into BNs a suitable notion of derivation. We also discuss modeling issues underlying the choice of Horn-based models vs BNs, by making explicit the \u201ccompletion semantics\u201d underlying a BN. This correspondence between \u201ccompleted\u201d Horn theories and BNs allows us to formally justify classical diagnostic schemata adopted for BNs
A Bayesian network can be used to model consisely the probabilistic knowledge with respect to a give...
Upon engineering a Bayesian network for the early detection of Classical Swine Fever in pigs, we fou...
Bayesian networks are a very general and powerful tool that can be used for a large number of proble...
The aim of the paper is to formally relate logical Horn models and Bayesian Networks (BNs) in the fr...
Bayesian networks have established themselves as an indispensable tool in artificial intelligence, ...
An important issue in the use of expert systems is the so-called brittleness problem. Expert systems...
Bayesian network (BN), also known as probability belief network, causal network [1] [2] [3], is a gr...
Bayesian Belief Networks are a powerful tool for combining different knowledge sources with various ...
The paper presents a casual-probabilistic approach to the technical diagnosis in which the solution ...
Bayesian networks are mathematically and statistically rigorous techniques for handling uncertainty....
AbstractModel-based diagnosis concerns using a model of the structure and behaviour of a system or d...
Includes bibliographical references (page 48).San Diego State University copy: the accompanying CD-R...
Bayesian networks (BN) are a valid method to analyze causal dependencies with uncertainties and to c...
Bayesian networks are a very general and powerful tool that can be used for a large number of proble...
Probabilistic logics have attracted a great deal of attention during the past few years. Where logic...
A Bayesian network can be used to model consisely the probabilistic knowledge with respect to a give...
Upon engineering a Bayesian network for the early detection of Classical Swine Fever in pigs, we fou...
Bayesian networks are a very general and powerful tool that can be used for a large number of proble...
The aim of the paper is to formally relate logical Horn models and Bayesian Networks (BNs) in the fr...
Bayesian networks have established themselves as an indispensable tool in artificial intelligence, ...
An important issue in the use of expert systems is the so-called brittleness problem. Expert systems...
Bayesian network (BN), also known as probability belief network, causal network [1] [2] [3], is a gr...
Bayesian Belief Networks are a powerful tool for combining different knowledge sources with various ...
The paper presents a casual-probabilistic approach to the technical diagnosis in which the solution ...
Bayesian networks are mathematically and statistically rigorous techniques for handling uncertainty....
AbstractModel-based diagnosis concerns using a model of the structure and behaviour of a system or d...
Includes bibliographical references (page 48).San Diego State University copy: the accompanying CD-R...
Bayesian networks (BN) are a valid method to analyze causal dependencies with uncertainties and to c...
Bayesian networks are a very general and powerful tool that can be used for a large number of proble...
Probabilistic logics have attracted a great deal of attention during the past few years. Where logic...
A Bayesian network can be used to model consisely the probabilistic knowledge with respect to a give...
Upon engineering a Bayesian network for the early detection of Classical Swine Fever in pigs, we fou...
Bayesian networks are a very general and powerful tool that can be used for a large number of proble...