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

Gaag, Linda C. van der; Bolt, Janneke H.; Loeffen, W.L.A.; Elbers, A.R.W.

doi:10.1007/978-3-642-14049-5_69

Cited by 4 publications

(1 citation statement)

References 7 publications

(6 reference statements)

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“…There have been a number of instances of BN implementation related to automated monitoring in agriculture, including the monitoring of animal livestock [30], crops [31], natural resources [32] and storage environment control [33]. The decisions supported were treatment regimes for mastitis in cattle, swine fever in pigs [34] and tropical diseases in bovine herds. The ability of BNs to make inferences has been leveraged to predict crop yields, disease evolution, disease transmission, weed infestations, pollution po-tential, the viability of farming businesses, breeding strategies, crop disease and agricultural policies [24].…”

Section: Bayesian Network For Agriculturementioning

confidence: 99%

A Decision Support System for Sustainable Agriculture and Food Loss Reduction under Uncertain Agricultural Policy Frameworks

Barons,

Walsh,

Salakpi

et al. 2024

Agriculture

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The EU Green Deal requires the reduction in pesticides and fertilisers in food crop production, whilst the sustainable development goals require reductions in food loss and food waste. In a complex and interacting system like the food system, these goals are difficult to coordinate. Here, we show an approach using Bayesian network modelling for decision support. Bayesian networks are important tools for modelling complex systems which may develop emergent behaviour and for providing quantitative comparisons for different candidate policies, approaches or interventions under the Integrating Decision Support System paradigm. Using lettuce as an exemplar crop, we demonstrate that expected food loss changes under different agricultural input reduction and integrated pest management combinations can be quantified to aid decision making for growers.

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Section: Bayesian Network For Agriculturementioning

confidence: 99%

A Decision Support System for Sustainable Agriculture and Food Loss Reduction under Uncertain Agricultural Policy Frameworks

Barons,

Walsh,

Salakpi

et al. 2024

Agriculture

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On Stopping Evidence Gathering for Diagnostic Bayesian Networks

Gaag

Bodlaender

2011

Lecture Notes in Computer Science

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Not just data: A method for improving prediction with knowledge

Yet

Perkins

Fenton

et al. 2014

Journal of Biomedical Informatics

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Many medical conditions are only indirectly observed through symptoms and tests. Developing predictive models for such conditions is challenging since they can be thought of as 'latent' variables. They are not present in the data and often get confused with measurements. As a result, building a model that fits data well is not the same as making a prediction that is useful for decision makers. In this paper, we present a methodology for developing Bayesian network (BN) models that predict and reason with latent variables, using a combination of expert knowledge and available data. The method is illustrated by a case study into the prediction of acute traumatic coagulopathy (ATC), a disorder of blood clotting that significantly increases the risk of death following traumatic injuries. There are several measurements for ATC and previous models have predicted one of these measurements instead of the state of ATC itself. Our case study illustrates the advantages of models that distinguish between an underlying latent condition and its measurements, and of a continuing dialogue between the modeller and the domain experts as the model is developed using knowledge as well as data.

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

Cited by 4 publications

References 7 publications

A Decision Support System for Sustainable Agriculture and Food Loss Reduction under Uncertain Agricultural Policy Frameworks

A Decision Support System for Sustainable Agriculture and Food Loss Reduction under Uncertain Agricultural Policy Frameworks

On Stopping Evidence Gathering for Diagnostic Bayesian Networks

Not just data: A method for improving prediction with knowledge

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