Hailfinder: A Bayesian system for forecasting severe weather

Abramson, Bruce; Brown, John M.; Edwards, Ward; Murphy, Allan H.; Winkler, Robert L.

doi:10.1016/0169-2070(95)00664-8

Cited by 151 publications

(90 citation statements)

References 8 publications

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“…Three models that contained sufficiently large CPTs which were defined without parametric distributions were available to us: ALARM [12], HAILFINDER [11] and HEPAR II [8]. We verified by contacting the authors of these models that none of the CPTs in these networks were specified using the noisy-OR/MAX assumption.…”

Section: How Common Are Noisy-max Gates In Real Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Knowledge Engineering for Bayesian Networks: How Common Are Noisy-MAX Distributions in Practice?

Zagorecki

Drużdżel

2013

IEEE Trans. Syst. Man Cybern, Syst.

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Abstract.One problem faced in knowledge engineering for Bayesian networks is the exponential growth of the number of parameters in their conditional probability tables (CPTs). The most common practical solution is application of the noisy-OR (or their generalization, the noisy-MAX) gates, which take advantage of independence of causal interactions and provide a logarithmic reduction of the number of parameters required to specify a CPT. In this paper, we propose an algorithm that fits a noisy-MAX distribution to an existing CPT and we apply it to search for noisy-MAX gates in three existing practical Bayesian networks. We show that noisy-MAX gate provides a surprisingly good fit for as many as 50% of CPTs in these networks. The importance of this finding is that it provides an empirical justification for the use of the noisy-MAX gate as a powerful knowledge engineering tool.

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Section: How Common Are Noisy-max Gates In Real Modelsmentioning

confidence: 99%

“…We analyze CPTs in three existing sizeable Bayesian network models: ALARM [11], HAILFINDER [12] and HEPAR II [8]. We show that the noisy-MAX gate provides a surprisingly good fit for a significant percentage of distributions in these networks.…”

Section: Introductionmentioning

confidence: 99%

Knowledge Engineering for Bayesian Networks: How Common Are Noisy-MAX Distributions in Practice?

Zagorecki

Drużdżel

2013

IEEE Trans. Syst. Man Cybern, Syst.

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“…The Insurance network (Figure 2) contains 27 variables and 52 arcs. Hailfinder [1] is a normative system that forecasts severe summer hail in northeastern Colorado. The Hailfinder network contains 56 variables and 66 arcs.…”

Section: Resultsmentioning

confidence: 99%

On the Use of Restrictions for Learning Bayesian Networks

Campos

Castellano

2005

Lecture Notes in Computer Science

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Abstract. In this paper we explore the use of several types of structural restrictions within algorithms for learning Bayesian networks. These restrictions may codify expert knowledge in a given domain, in such a way that a Bayesian network representing this domain should satisfy them. Our objective is to study whether the algorithms for automatically learning Bayesian networks from data can benefit from this prior knowledge to get better results. We formally define three types of restrictions: existence of arcs and/or edges, absence of arcs and/or edges, and ordering restrictions, and also study their interactions and how they can be managed within Bayesian network learning algorithms based on the score+search paradigm. Then we particularize our study to the classical local search algorithm with the operators of arc addition, arc removal and arc reversal, and carry out experiments using this algorithm on several data sets.

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“…The idea of introducing hidden variables in a Bayesian network was described before for the Hailfinder model [4]. In that model, a single hidden variable was introduced as an approach to managing the complexity of the set of observations: the hidden variable was used to abstract from the details hidden in the evidence.…”

Section: Introductionmentioning

confidence: 99%

Modelling Patterns of Evidence in Bayesian Networks: A Case-Study in Classical Swine Fever

Gaag

Bolt

Loeffen

et al. 2010

Computational Intelligence for Knowledge-Based Systems Design

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Abstract. Upon engineering a Bayesian network for the early detection of Classical Swine Fever in pigs, we found that the commonly used approach of separately modelling the relevant observable variables would not suffice to arrive at satisfactory performance of the network: explicit modelling of combinations of observations was required to allow identifying and reasoning about patterns of evidence. In this paper, we outline a general approach to modelling relevant patterns of evidence in a Bayesian network. We demonstrate its application for our problem domain and show that it served to significantly improve our network's performance.

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Hailfinder: A Bayesian system for forecasting severe weather

Cited by 151 publications

References 8 publications

Knowledge Engineering for Bayesian Networks: How Common Are Noisy-MAX Distributions in Practice?

Knowledge Engineering for Bayesian Networks: How Common Are Noisy-MAX Distributions in Practice?

On the Use of Restrictions for Learning Bayesian Networks

Modelling Patterns of Evidence in Bayesian Networks: A Case-Study in Classical Swine Fever

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