Add to ORKGAbstract. While Bayesian network models may contain a handful of numerical parameters that are important for their quality, several em-pirical studies have confirmed that overall precision of their probabilities is not crucial. In this paper, we study the impact of the structure of a Bayesian network on the precision of medical diagnostic systems. We show that also the structure is not that important – diagnostic accuracy of several medical diagnostic models changes minimally when we subject their structures to such transformations as arc removal and arc reversal
A Bayesian network is a probabilistic graphical model that represents a set of variables and their c...
Bayesian networks have proven their value in solving complex diagnostic problems. The main bottlenec...
<p>The number next to each directed arc of the BN indicates the confidence (posterior probability) i...
While Bayesian network models may contain a handful of numerical parameters that are important for t...
Objective: One of the hardest technical tasks in employing Bayesian network models in practice is ob...
While most knowledge engineers believe that the quality of\ud results obtained by means of Bayesian ...
Abstract-Bayesian networks have been very useful as models for computerized diagnostic assistants, a...
common belief is that a Bayesian network may achieve better performance with a more complex structur...
Computer-based diagnostic decision support systems (DSS) will play an increasingly important role in...
Bridging the gap between the theory of Bayesian networks and solving an actual problem is still a bi...
Abstract. Bayesian network structures are usually built using only the data and starting from an emp...
A Bayesian network can be used to model consisely the probabilistic knowledge with respect to a give...
Diagnostic reasoning in essence amounts to reasoning about an unobservable condition, based on indir...
In medical diagnosis a proper uncertainty calculus is crucial in knowledge representation. Finite c...
AbstractBayesian belief networks are being increasingly used as a knowledge representation for reaso...
A Bayesian network is a probabilistic graphical model that represents a set of variables and their c...
Bayesian networks have proven their value in solving complex diagnostic problems. The main bottlenec...
<p>The number next to each directed arc of the BN indicates the confidence (posterior probability) i...
While Bayesian network models may contain a handful of numerical parameters that are important for t...
Objective: One of the hardest technical tasks in employing Bayesian network models in practice is ob...
While most knowledge engineers believe that the quality of\ud results obtained by means of Bayesian ...
Abstract-Bayesian networks have been very useful as models for computerized diagnostic assistants, a...
common belief is that a Bayesian network may achieve better performance with a more complex structur...
Computer-based diagnostic decision support systems (DSS) will play an increasingly important role in...
Bridging the gap between the theory of Bayesian networks and solving an actual problem is still a bi...
Abstract. Bayesian network structures are usually built using only the data and starting from an emp...
A Bayesian network can be used to model consisely the probabilistic knowledge with respect to a give...
Diagnostic reasoning in essence amounts to reasoning about an unobservable condition, based on indir...
In medical diagnosis a proper uncertainty calculus is crucial in knowledge representation. Finite c...
AbstractBayesian belief networks are being increasingly used as a knowledge representation for reaso...
A Bayesian network is a probabilistic graphical model that represents a set of variables and their c...
Bayesian networks have proven their value in solving complex diagnostic problems. The main bottlenec...
<p>The number next to each directed arc of the BN indicates the confidence (posterior probability) i...