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Entropy
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Bayesian Network Modelling in Data Sparse Environments
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Bayesian Network Modelling in Data Sparse Environments

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: closed (15 September 2024) | Viewed by 7277

Special Issue Editors

Dr. Anca Hanea

E-Mail Website
Guest Editor
Centre of Excellence for Biosecurity Risk Analysis, School of BioSciences, The University of Melbourne, Parkville, VIC 3010, Australia
Interests: probabilistic modelling; applied maths; risk analysis; decision making under uncertainty; uncertainty quantification; structured expert judgement; elicitation protocols
Dr. Tina Nane

E-Mail Website
Guest Editor
Delft Institute of Applied Mathematics (DIAM), Delft University of Technology, 2628 CD Delft, The Netherlands
Interests: structured expert judgement; decision science; Bayesian networks; uncertainty quantification

Special Issue Information

Dear Colleagues,

Bayesian networks (BNs) are graphical representations (i.e., directed acyclic graphs, DAGs) of the joint probability distribution of dependent variables. The DAG captures (conditional) independencies among variables, which enables a convenient factorization of the joint distribution. BNs have found applications in many diverse domains.

Building BNs consists of two main steps: (1) structure specification and (2) domain-specific parameterization. However, these steps are iterative when communicated to stakeholders, monitored and reviewed. They are frequently refined using domain experts’ input.

Both structure and parameters can be obtained either from data or experts, but they are typically obtained using a combination of both. Despite the current data-rich environment, often there are insufficient data to evaluate potential future events, risks, or opportunities, or to represent their interactions.

While formal protocols exist to quantify parameters in data-sparse environments, there is a gap in well-defined procedures for DAG construction. More research is required to appropriately address the inherent subjectivity involved in constructing BNs in such environments. Moreover, transparency and rigor in reporting, documenting, and justifying all choices made during the BN modeling process should be made a priority.

We invite submissions, including original research articles and reviews, both from an applied perspective as well as methodological developments relating to all issues outlined above.

Dr. Anca Hanea
Dr. Tina Nane
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Bayesian networks
  • expert judgement
  • elicitation protocols
  • dependence modelling
  • uncertainty analysis
  • data-sparse environments

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Published Papers (6 papers)

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Research

26 pages, 348 KiB  
Article
Reporting Standards for Bayesian Network Modelling
by Martine J. Barons, Anca M. Hanea, Steven Mascaro and Owen Woodberry
Entropy 2025, 27(1), 69; https://doi.org/10.3390/e27010069 - 15 Jan 2025
Viewed by 923
Abstract
Reproducibility is a key measure of the veracity of a modelling result or finding. In other research areas, notably in medicine, reproducibility is supported by mandating the inclusion of an agreed set of details into every research publication, facilitating systematic reviews, transparency and [...] Read more.
Reproducibility is a key measure of the veracity of a modelling result or finding. In other research areas, notably in medicine, reproducibility is supported by mandating the inclusion of an agreed set of details into every research publication, facilitating systematic reviews, transparency and reproducibility. Governments and international organisations are increasingly turning to modelling approaches in the development and decision-making for policy and have begun asking questions about accountability in model-based decision making. The ethical issues of relying on modelling that is biased, poorly constructed, constrained by heroic assumptions and not reproducible are multiplied when such models are used to underpin decisions impacting human and planetary well-being. Bayesian Network modelling is used in policy development and decision support across a wide range of domains. In light of the recent trend for governments and other organisations to demand accountability and transparency, we have compiled and tested a reporting checklist for Bayesian Network modelling which will bring the desirable level of transparency and reproducibility to enable models to support decision making and allow the robust comparison and combination of models. The use of this checklist would support the ethical use of Bayesian network modelling for impactful decision making and research. Full article
(This article belongs to the Special Issue Bayesian Network Modelling in Data Sparse Environments)
34 pages, 459 KiB  
Article
Dynamic Bayesian Networks, Elicitation, and Data Embedding for Secure Environments
by Kieran Drury and Jim Q. Smith
Entropy 2024, 26(11), 985; https://doi.org/10.3390/e26110985 - 17 Nov 2024
Cited by 1 | Viewed by 926
Abstract
Serious crime modelling typically needs to be undertaken securely behind a firewall where police knowledge and capabilities remain undisclosed. Data informing an ongoing incident are often sparse; a large proportion of relevant data only come to light after the incident culminates or after [...] Read more.
Serious crime modelling typically needs to be undertaken securely behind a firewall where police knowledge and capabilities remain undisclosed. Data informing an ongoing incident are often sparse; a large proportion of relevant data only come to light after the incident culminates or after police intervene—by which point it is too late to make use of the data to aid real-time decision-making for the incident in question. Much of the data that are available to the police to support real-time decision-making are highly confidential and cannot be shared with academics, and are therefore missing to them. In this paper, we describe the development of a formal protocol where a graphical model is used as a framework for securely translating a base model designed by an academic team to a fully embellished model for use by a police team. We then show, for the first time, how libraries of these models can be built and used for real-time decision support to circumvent the challenges of data missingness seen in such a secure environment through the ability to match ongoing plots to existing models within the library.The parallel development described by this protocol ensures that any sensitive information collected by police and missing to academics remains secured behind a firewall. The protocol nevertheless guides police so that they are able to combine the typically incomplete data streams that are open source with their more sensitive information in a formal and justifiable way. We illustrate the application of this protocol by describing how a new entry—a suspected vehicle attack—can be embedded into such a police library of criminal plots. Full article
(This article belongs to the Special Issue Bayesian Network Modelling in Data Sparse Environments)
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13 pages, 668 KiB  
Article
Sensitivity of Bayesian Networks to Errors in Their Structure
by Agnieszka Onisko and Marek J. Druzdzel
Entropy 2024, 26(11), 975; https://doi.org/10.3390/e26110975 - 14 Nov 2024
Viewed by 835
Abstract
There is a widespread belief in the Bayesian network (BN) community that while the overall accuracy of the results of BN inference is not sensitive to the precision of parameters, it is sensitive to the structure. We report on the results of a [...] Read more.
There is a widespread belief in the Bayesian network (BN) community that while the overall accuracy of the results of BN inference is not sensitive to the precision of parameters, it is sensitive to the structure. We report on the results of a study focusing on the parameters in a companion paper, while this paper focuses on the BN graphical structure. We present the results of several experiments in which we test the impact of errors in the BN structure on its accuracy in the context of medical diagnostic models. We study the deterioration in model accuracy under structural changes that systematically modify the original gold standard model, notably the node and edge removal and edge reversal. Our results confirm the popular belief that the BN structure is important, and we show that structural errors may lead to a serious deterioration in the diagnostic accuracy. At the same time, most BN models are forgiving to single errors. In light of these results and the results of the companion paper, we recommend that knowledge engineers focus their efforts on obtaining a correct model structure and worry less about the overall precision of parameters. Full article
(This article belongs to the Special Issue Bayesian Network Modelling in Data Sparse Environments)
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17 pages, 610 KiB  
Article
Sensitivity of Bayesian Networks to Noise in Their Parameters
by Agnieszka Onisko and Marek J. Druzdzel
Entropy 2024, 26(11), 963; https://doi.org/10.3390/e26110963 - 9 Nov 2024
Cited by 1 | Viewed by 896
Abstract
There is a widely spread belief in the Bayesian network (BN) community that the overall accuracy of results of BN inference is not too sensitive to the precision of their parameters. We present the results of several experiments in which we put this [...] Read more.
There is a widely spread belief in the Bayesian network (BN) community that the overall accuracy of results of BN inference is not too sensitive to the precision of their parameters. We present the results of several experiments in which we put this belief to a test in the context of medical diagnostic models. We study the deterioration of accuracy under random symmetric noise but also biased noise that represents overconfidence and underconfidence of human experts.Our results demonstrate consistently, across all models studied, that while noise leads to deterioration of accuracy, small amounts of noise have minimal effect on the diagnostic accuracy of BN models. Overconfidence, common among human experts, appears to be safer than symmetric noise and much safer than underconfidence in terms of the resulting accuracy. Noise in medical laboratory results and disease nodes as well as in nodes forming the Markov blanket of the disease nodes has the largest effect on accuracy. In light of these results, knowledge engineers should moderately worry about the overall quality of the numerical parameters of BNs and direct their effort where it is most needed, as indicated by sensitivity analysis. Full article
(This article belongs to the Special Issue Bayesian Network Modelling in Data Sparse Environments)
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17 pages, 20240 KiB  
Article
Foundational Aspects for Incorporating Dependencies in Copula-Based Bayesian Networks Using Structured Expert Judgments, Exemplified by the Ice Sheet–Sea Level Rise Elicitation
by Dorota Kurowicka, Willy Aspinall and Roger Cooke
Entropy 2024, 26(11), 949; https://doi.org/10.3390/e26110949 - 5 Nov 2024
Viewed by 889
Abstract
The work presented here marks a further advance in expert uncertainty quantification. In a recent probabilistic evaluation of ice sheet process contributions to sea level rise, tail dependence was elicited and propagated through an uncertainty analysis for the first time. The elicited correlations [...] Read more.
The work presented here marks a further advance in expert uncertainty quantification. In a recent probabilistic evaluation of ice sheet process contributions to sea level rise, tail dependence was elicited and propagated through an uncertainty analysis for the first time. The elicited correlations and tail dependencies concerned pairings of three processes: Accumulation, Discharge and Run-off, which operate on major ice sheets in the West and East Antarctic and in Greenland. The elicitation enumerated dependencies between these processes under selected global temperature change scenarios over different future time horizons. These expert judgments allowed us to populate a Paired Copula Bayesian network model to obtain the estimated contributions of these ice sheets for future sea level rise. Including positive central tendency dependence and tail dependence increases the fatness of the upper tails of projected sea level rise distributions, an amplification important for designing and evaluating possible mitigation strategies. Detailing and jointly computing distributional dependencies and tail dependencies can be crucial components of good practice for assessing the influence of uncertainties on extreme values when modelling stochastic multifactorial processes. Full article
(This article belongs to the Special Issue Bayesian Network Modelling in Data Sparse Environments)
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31 pages, 5366 KiB  
Article
Elicitation of Rank Correlations with Probabilities of Concordance: Method and Application to Building Management
by Benjamin Ramousse, Miguel Angel Mendoza-Lugo, Guus Rongen and Oswaldo Morales-Nápoles
Entropy 2024, 26(5), 360; https://doi.org/10.3390/e26050360 - 25 Apr 2024
Viewed by 1329
Abstract
Constructing Bayesian networks (BN) for practical applications presents significant challenges, especially in domains with limited empirical data available. In such situations, field experts are often consulted to estimate the model’s parameters, for instance, rank correlations in Gaussian copula-based Bayesian networks (GCBN). Because there [...] Read more.
Constructing Bayesian networks (BN) for practical applications presents significant challenges, especially in domains with limited empirical data available. In such situations, field experts are often consulted to estimate the model’s parameters, for instance, rank correlations in Gaussian copula-based Bayesian networks (GCBN). Because there is no consensus on a ‘best’ approach for eliciting these correlations, this paper proposes a framework that uses probabilities of concordance for assessing dependence, and the dependence calibration score to aggregate experts’ judgments. To demonstrate the relevance of our approach, the latter is implemented to populate a GCBN intended to estimate the condition of air handling units’ components—a key challenge in building asset management. While the elicitation of concordance probabilities was well received by the questionnaire respondents, the analysis of the results reveals notable disparities in the experts’ ability to quantify uncertainty. Moreover, the application of the dependence calibration aggregation method was hindered by the absence of relevant seed variables, thus failing to evaluate the participants’ field expertise. All in all, while the authors do not recommend to use the current model in practice, this study suggests that concordance probabilities should be further explored as an alternative approach for the elicitation of dependence. Full article
(This article belongs to the Special Issue Bayesian Network Modelling in Data Sparse Environments)
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