Probabilities for a probabilistic network: a case study in oesophageal cancer

Gaag, Linda C. van der; Renooij, Silja; Witteman, Cilia; Aleman, B.M.P.; Taal, Babs G.

doi:10.1016/s0933-3657(02)00012-x

Cited by 119 publications

(67 citation statements)

References 12 publications

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“…This means to specify the strength of the relations defined by G using the CPDs of P. For each variable v ∈ V, we specify the CPD P (V |pa(V )) of V conditional on pa(V ) ⊆ V. When sufficient hard data to perform parameter estimation is not available, it is necessary to assess the probability values in a different way. There are a number of techniques that can be used to elicit probability values from domain experts [33] including direct, e.g., [34] and indirect methods, e.g., [35]. An common alternative to elicitation from experts, is to use literature, mathematical formulas to specify the relationship between a variable V and its direct parents pa(V ) or methods as described above.…”

Section: Implementmentioning

confidence: 99%

A methodology for developing local smart diagnostic models using expert knowledge

Madsen

Søndberg-Jeppesen

Lohse

et al. 2015

2015 IEEE 13th International Conference on Industrial Informatics (INDIN)

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Abstract-This paper describes an innovative modular component-based modelling approach for diagnostics and condition-monitoring of manufacturing equipment. The approach is based on the use of object-oriented Bayesian networks, which supports a natural decomposition of a large and complex system into a set of less complex components. The methodology consists of six steps supporting the development process: Begin, Design, Implement, Test, Analyse, and Deploy. The process is iterative and the steps should be repeated until a satisfactory model has been achieved. The paper describes the details of the methodology as well as illustrates the use of the componentbased modelling approach on a linear axis used in manufacturing. This application demonstrates the power and flexibility of the approach for diagnostics and condition-monitoring and shows a significant potential of the approach for modular componentbased modelling in manufacturing and other domains.

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Section: Implementmentioning

confidence: 99%

A methodology for developing local smart diagnostic models using expert knowledge

Madsen

Søndberg-Jeppesen

Lohse

et al. 2015

2015 IEEE 13th International Conference on Industrial Informatics (INDIN)

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“…In communicating a conditional probability to our domain, we do not use mathematical notation -the requested probability is translated into a fragment of text instead. To support the experts in their assessment task, a verbalnumerical sliding response scale [15] was provived.…”

Section: Model Parametersmentioning

confidence: 99%

A Bayesian network model for the diagnosis of the caring procedure for wheelchair users with spinal injury

Athanasiou

Clark

2009

Computer Methods and Programs in Biomedicine

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“…With the variables are associated conditional probabilities that describe the strengths of the influences between the variables. The oesophagus network currently comprises over 70 statistical variables for which more than 4000 conditional probabilities have been specified [4].…”

Section: The Oesophagus Networkmentioning

confidence: 99%

Experiences with Modelling Issues in Building Probabilistic Networks

Gaag

Helsper

2002

Knowledge Engineering and Knowledge Management: Ontologies and the Semantic Web

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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 probabilistic network for a complex medical domain, we have encountered and resolved numerous non-trivial modelling issues. Since many of these issues 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.

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Probabilities for a probabilistic network: a case study in oesophageal cancer

Cited by 119 publications

References 12 publications

A methodology for developing local smart diagnostic models using expert knowledge

A methodology for developing local smart diagnostic models using expert knowledge

A Bayesian network model for the diagnosis of the caring procedure for wheelchair users with spinal injury

Experiences with Modelling Issues in Building Probabilistic Networks

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