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Parameterising Bayesian Networks [chapter]

Owen Woodberry, Ann E. Nicholson, Kevin B. Korb, Carmel Pollino
2004 Lecture Notes in Computer Science  
Most documented Bayesian network (BN) applications have been built through knowledge elicitation from domain experts (DEs).  ...  There is a further need for combining what can be learned from the data with what can be elicited from DEs.  ...  BAYESIAN NETWORKS Background A BN is a graphical representation of a joint probability distribution over a set of statistical variables.  ... 
doi:10.1007/978-3-540-30549-1_108 fatcat:73ul2wvoo5aqbnmcuglz63rvj4

Advances in Bayesian network modelling: Integration of modelling technologies

Bruce G. Marcot, Trent D. Penman
2019 Environmental Modelling & Software  
Advances include improving areas of Bayesian classifiers and machine-learning algorithms for model structuring and parameterization, and development of time-dynamic models.  ...  Increasingly, BN models are being integrated with: management decision networks; structural equation modeling of causal networks; Bayesian neural networks; combined discrete and continuous variables; object-oriented  ...  Acknowledgments Inspiration for this paper comes from a keynote address given by the senior author in 2017 at the Joint Conference of the Australasian Bayesian Network Modelling Society and the Society  ... 
doi:10.1016/j.envsoft.2018.09.016 fatcat:r3r75adpbva3lbqfl5fijmg7ki

Development of a stakeholder-driven spatial modeling framework for strategic landscape planning using Bayesian networks across two urban-rural gradients in Maine, USA

Spencer R. Meyer, Michelle L. Johnson, Robert J. Lilieholm, Christopher S. Cronan
2014 Ecological Modelling  
We elicited stakeholder knowledge to: (1) identify generalized drivers of land use change; (2) construct Bayesian network models of suitability for each of the four land uses based on site-level factors  ...  ownerships, and must consider the combined influences of biophysical, economic, and social factors that affect land use decisions.  ...  Bayesian Networks and Land Use Suitability Bayesian networks (BNs) are decision support tools that use Bayes' probability theory to describe decision processes by estimating the joint probability of an  ... 
doi:10.1016/j.ecolmodel.2014.06.023 fatcat:dnz7cqbpx5dl7aaf6espaicjee

Expert Elicitation and Data Noise Learning for Material Flow Analysis using Bayesian Inference [article]

Jiayuan Dong, Jiankan Liao, Xun Huan, Daniel Cooper
2022 arXiv   pre-print
We start to address these issues by first deriving and implementing an expert elicitation procedure suitable for generating MFA parameter priors.  ...  Bayesian inference allows the transparent communication of uncertainty in material flow analyses (MFAs), and a systematic update of uncertainty as new data become available.  ...  Distributions for Modeling the MFA Priors The next step is fitting the histograms elicited from the MFA experts to a family of parameterized PDFs.  ... 
arXiv:2207.09288v1 fatcat:tpgqlz4h3rfmvjpz2nsnxwj2vi

EcoQBNs: First Application of Ecological Modeling with Quantum Bayesian Networks

Bruce G. Marcot
2021 Entropy  
A recent advancement in modeling was the development of quantum Bayesian networks (QBNs).  ...  QBNs can solve a variety of problems which are unsolvable by, or are too complex for, traditional BNs.  ...  Conflicts of Interest: The author declares that the research was conducted in the absence of any commercial or financial relationships which could be construed as a potential conflict of interest.  ... 
doi:10.3390/e23040441 pmid:33918806 pmcid:PMC8069849 fatcat:ks2aatsydfgsthpniso66bi35m

Assessing Flood Risk Dynamics in Data-Scarce Environments—Experiences From Combining Impact Chains With Bayesian Network Analysis in the Lower Mono River Basin, Benin

Mario Wetzel, Lorina Schudel, Adrian Almoradie, Kossi Komi, Julien Adounkpè, Yvonne Walz, Michael Hagenlocher
2022 Frontiers in Water  
Particularly in combination with a Bayesian Network approach, the method enables an improved understanding of how different risk drivers interact within the system and allows for dynamic simulations of  ...  The study finds that impact chains are a useful model approach to conceptualize interactions of risk drivers.  ...  Second, the network was simplified where necessary by introducing auxiliary nodes (parent divorcing). (4) First parameterization For each CPT of the model first parameters were elicited.  ... 
doi:10.3389/frwa.2022.837688 fatcat:voii5h3e3bda3m5axlu3oymh3m

A Dynamic Bayesian Network Approach to Behavioral Modelling of Elderly People during a Home-based Augmented Reality Balance Physiotherapy Programme

Eleni I. Georga, Dimitrios Gatsios, Vasilis Tsakanikas, Konstantina D. Kourou, Matthew Liston, Marousa Pavlou, Dimitrios Kikidis, Athanasios Bibas, Christos Nikitas, Doris Eva Bamiou, Dimitrios I. Fotiadis
2020 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)  
In this study, we propose a dynamic Bayesian network (DBN)-based approach to behavioral modelling of community dwelling older adults at risk for falls during the daily sessions of a hologram-enabled vestibular  ...  The component of human behavior being modelled is the level of frustration experienced by the user at each exercise, as it is assessed by the NASA Task Load Index.  ...  both spatial and temporal dynamics in a system, and (iii) combining knowledge elicited by the experts and a parameterized (data-driven) solution.  ... 
doi:10.1109/embc44109.2020.9175435 pmid:33019234 fatcat:dxizf2zxyrethftyynk3f3za5a

Meta-interpreters for rule-based inference under uncertainty

Shimon Schocken, Tim Finin
1990 Decision Support Systems  
One of the key challenges in designing expert systems is a credible representation of uncertainty and partial belief.  ...  The purposes of this logic model is twofold: first, it provides a clear and concise conceptualization of belief representation and propagation in rule-based systems.  ...  At the same time, one would hope that the behavior of a CF-based expert system would be compatible with that of a Bayesian expert system, all other things held equal (including the expert and the knowledge-base  ... 
doi:10.1016/0167-9236(90)90006-d fatcat:uiyjt6nk2vfzrcyxrqzddlm24q

Perspectives on Incorporating Expert Feedback into Model Updates [article]

Valerie Chen, Umang Bhatt, Hoda Heidari, Adrian Weller, Ameet Talwalkar
2022 arXiv   pre-print
Machine learning (ML) practitioners are increasingly tasked with developing models that are aligned with non-technical experts' values and goals.  ...  We end with a set of open questions that naturally arise from our proposed taxonomy and subsequent survey.  ...  Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of any of these funding agencies.  ... 
arXiv:2205.06905v2 fatcat:5zfmhurisbc4zfxbcnxvw6suia

Mental models for conservation research and practice

Katie Moon, Angela M. Guerrero, Vanessa. M. Adams, Duan Biggs, Deborah A. Blackman, Luke Craven, Helen Dickinson, Helen Ross
2019 Conservation Letters  
Mental models are representations in people's minds of how parts of the world work.  ...  We begin by explaining some of the dominant applications of mental models in conservation: revealing individual assumptions about a system, developing a stakeholder-based model of the system, and creating  ...  Revealing connections between assumptions, preferences, and F I G U R E 1 Example of a digraph representation of a mental model of invasive species management elicited through influence diagram method.  ... 
doi:10.1111/conl.12642 fatcat:o7ponrdxfbhkddzbljasdqn7o4

A serious game to parameterize Bayesian networks: Validation in a case study in northeastern Madagascar

Enrico Celio, Ravosaina Ntsiva Nirinimanitra Andriatsitohaina, Julie Gwendolin Zaehringer
2019 Environmental Modelling & Software  
We show here how we used a serious game to parameterize a Bayesian network-based land-use decision model.  ...  We discuss how the success in validation quality may be related to the design of the game and conclude that the transfer from a game to Bayesian networks could improve the parameterization quality.  ...  This research was supported by the Swiss Programme for Research on Global Issues for Development (r4d Programme), supported by the Swiss National Science Foundation (SNSF) and the Swiss Agency for Development  ... 
doi:10.1016/j.envsoft.2019.104525 fatcat:qyb7wjrk6vcjnd3s4vbu2447ii

Prior knowledge elicitation: The past, present, and future [article]

Petrus Mikkola, Osvaldo A. Martin, Suyog Chandramouli, Marcelo Hartmann, Oriol Abril Pla, Owen Thomas, Henri Pesonen, Jukka Corander, Aki Vehtari, Samuel Kaski, Paul-Christian Bürkner, Arto Klami
2021 arXiv   pre-print
Specification of the prior distribution for a Bayesian model is a central part of the Bayesian workflow for data analysis, but it is often difficult even for statistical experts.  ...  We analyze the state of the art by identifying a range of key aspects of prior knowledge elicitation, from properties of the modelling task and the nature of the priors to the form of interaction with  ...  Acknowledgments This work was supported by the Academy of Finland  ... 
arXiv:2112.01380v1 fatcat:vnstcxuaezgjrpdyikhleyat2m

Participatory development of a Bayesian network model for catchment-based water resource management

T. Chan, H. Ross, S. Hoverman, B. Powell
2010 Water Resources Research  
Further elicitation of quantitative aspects took place with a subset of water management professionals for development into a working Bayesian network model.  ...  A participatory approach was used to develop a Bayesian network model for assisting integration of water resource management in the Kongulai catchment in the Solomon Islands.  ...  Conceptual diagram converted into a Bayesian network with expert elicited prior conditional probability tables and expectation-maximization learning with available quantitative data.  ... 
doi:10.1029/2009wr008848 fatcat:27gtmth6tzhdvoq76juuul6jua

Measures of Cognitive Distance and Diversity

Johannes Castner
2014 Social Science Research Network  
I use a model of human causal learning, Causal Support (Tenenbaum & Griffiths, 2001) , to derive a meaningful measure of Cognitive Distance-the degree to which two people differ in their opinions about  ...  the workings of the world.  ...  Bayesian Networks were recently suggested by Tenenbaum and Griffiths as general representations of human cognitive models, regarding some causal system (Tenenbaum & Griffiths, 2008) .  ... 
doi:10.2139/ssrn.2477484 fatcat:w6bil77xhnbtrl7neoio5g4lja

The conceptual foundation of environmental decision support

Peter Reichert, Simone D. Langhans, Judit Lienert, Nele Schuwirth
2015 Journal of Environmental Management  
sub-objectives from experts, and the consideration of uncertainty in value and utility elicitation.  ...  We illustrate aspects of the suggested methodology by its application to river management in general and with a small, didactical case study on spatial river rehabilitation prioritization.  ...  Nele Schuwirth was partially funded by the Swiss National Science Foundation in the scope of the project "iWaQa" in the National Research Program 61 on Sustainable Water Management (grant no. 4061-40_125866  ... 
doi:10.1016/j.jenvman.2015.01.053 pmid:25748599 fatcat:t6tbatgiafhjrhcwpiracos5kq
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