Abstract. Building a probabilistic network for a real-life application is a difficult and time-consuming task. Methodologies for building such a network, however, are still lacking. Also, literature on network-specific modelling issues is quite scarce. As we have developed a large proba-bilistic network for a complex medical domain, we have encountered and resolved numerous non-trivial modelling issues. Since many of these is-sues pertain not only to our application but are likely to emerge for other applications as well, we feel that sharing them will contribute to engineering probabilistic networks in general.
Accurate epidemiological models require parameter estimates that account for mobility patterns and s...
Bayesian networks are a popular mechanism for dealing with uncertainty in complex situations. They a...
Redes probabilísticas são modelos muito versáteis, com aplicabilidade crescente em diversas áreas. E...
Probabilistic networks are now fairly well established as practical representations of knowl-edge fo...
Probabilistic networks are now fairly well established as practical representations of knowledge for...
Probabilistic Modelling in Bioinformatics and Medical Informatics has been written for researchers a...
As empirical data collection and inference is often an imperfect process, and many systems can be re...
This chapter introduces a probabilistic approach to modelling in physiology and medicine: the quanti...
Abstract. Building a probabilistic network for a real-life domain of ap-plication is a hard and time...
Abstract. Building a probabilistic network for a real-life domain of application is a hard and time-...
One of the most difficult obstacles in the practical application of probabilistic methods is the eff...
Research on probabilistic models of networks now spans a wide variety of fields, including physics, ...
A probabilistic network consists of a graphical representation (a directed graph) of the important v...
With the help of two experts in gastrointestinal oncology from The Netherlands Cancer Institute, Ant...
Accurate epidemiological models require parameter estimates that account for mobility patterns and s...
Accurate epidemiological models require parameter estimates that account for mobility patterns and s...
Bayesian networks are a popular mechanism for dealing with uncertainty in complex situations. They a...
Redes probabilísticas são modelos muito versáteis, com aplicabilidade crescente em diversas áreas. E...
Probabilistic networks are now fairly well established as practical representations of knowl-edge fo...
Probabilistic networks are now fairly well established as practical representations of knowledge for...
Probabilistic Modelling in Bioinformatics and Medical Informatics has been written for researchers a...
As empirical data collection and inference is often an imperfect process, and many systems can be re...
This chapter introduces a probabilistic approach to modelling in physiology and medicine: the quanti...
Abstract. Building a probabilistic network for a real-life domain of ap-plication is a hard and time...
Abstract. Building a probabilistic network for a real-life domain of application is a hard and time-...
One of the most difficult obstacles in the practical application of probabilistic methods is the eff...
Research on probabilistic models of networks now spans a wide variety of fields, including physics, ...
A probabilistic network consists of a graphical representation (a directed graph) of the important v...
With the help of two experts in gastrointestinal oncology from The Netherlands Cancer Institute, Ant...
Accurate epidemiological models require parameter estimates that account for mobility patterns and s...
Accurate epidemiological models require parameter estimates that account for mobility patterns and s...
Bayesian networks are a popular mechanism for dealing with uncertainty in complex situations. They a...
Redes probabilísticas são modelos muito versáteis, com aplicabilidade crescente em diversas áreas. E...