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Journal articles on the topic 'Bayesian belief network'

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Published: 4 June 2021

Last updated: 20 February 2023

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1

Herskovits, E. H., and G. F. Cooper. "Algorithms for Bayesian Belief-Network Precomputation." Methods of Information in Medicine 30, no. 02 (1991): 81–89. http://dx.doi.org/10.1055/s-0038-1634820.

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AbstractBayesian belief networks provide an intuitive and concise means of representing probabilistic relationships among the variables in expert systems. A major drawback to this methodology is its computational complexity. We present an introduction to belief networks, and describe methods for precomputing, or caching, part of a belief network based on metrics of probability and expected utility. These algorithms are examples of a general method for decreasing expected running time for probabilistic inference.We first present the necessary background, and then present algorithms for producing caches based on metrics of expected probability and expected utility. We show how these algorithms can be applied to a moderately complex belief network, and present directions for future research.

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2

Shek, T. W. "Bayesian Belief Network in histopathology." Journal of Clinical Pathology 49, no. 10 (October 1, 1996): 864. http://dx.doi.org/10.1136/jcp.49.10.864-b.

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3

LIN, YAN, and MAREK J. DRUZDZEL. "RELEVANCE-BASED INCREMENTAL BELIEF UPDATING IN BAYESIAN NETWORKS." International Journal of Pattern Recognition and Artificial Intelligence 13, no. 02 (March 1999): 285–95. http://dx.doi.org/10.1142/s0218001499000161.

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Relevance reasoning in Bayesian networks can be used to improve efficiency of belief updating algorithms by identifying and pruning those parts of a network that are irrelevant for computation. Relevance reasoning is based on the graphical property of d-separation and other simple and efficient techniques, the computational complexity of which is usually negligible when compared to the complexity of belief updating in general. This paper describes a belief updating technique based on relevance reasoning that is applicable in practical systems in which observations and model revisions are interleaved with belief updating. Our technique invalidates the posterior beliefs of those nodes that depend probabilistically on the new evidence or the revised part of the model and focuses the subsequent belief updating on the invalidated beliefs rather than on all beliefs. Very often observations and model updating invalidate only a small fraction of the beliefs and our scheme can then lead to sub stantial savings in computation. We report results of empirical tests for incremental belief updating when the evidence gathering is interleaved with reasoning. These tests demonstrate the practical significance of our approach.

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4

Yershov, S. V., and F. V. Kostukevich. "Modeling technology based on fuzzy object-oriented Bayesian belief networks." PROBLEMS IN PROGRAMMING, no. 2-3 (June 2016): 179–87. http://dx.doi.org/10.15407/pp2016.02-03.179.

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The basic components of information technology inductive modeling causation under uncertainty based on fuzzy object-oriented Bayesian networks is proposed. The technology is based on a combination of transformation algorithms Bayesian network in the junction tree. New more efficient algorithms for Bayesian network transformation are resulted from modifications known algorithms; algorithms based on the use of more information on the graphical representation of the network are considered. Structurally functional model are described, it is designed to implement the transformation of fuzzy object-oriented Bayesian networks.

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5

Xu, Jian-min, Shu-fang Wu, and Yu Hong. "Topic tracking with Bayesian belief network." Optik 125, no. 9 (May 2014): 2164–69. http://dx.doi.org/10.1016/j.ijleo.2013.10.044.

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6

Grimm, Veronika, and Friederike Mengel. "Experiments on Belief Formation in Networks." Journal of the European Economic Association 18, no. 1 (October 9, 2018): 49–82. http://dx.doi.org/10.1093/jeea/jvy038.

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Abstract We study belief formation in social networks using a laboratory experiment. Participants in our experiment observe an imperfect private signal on the state of the world and then simultaneously and repeatedly guess the state, observing the guesses of their network neighbors in each period. Across treatments we vary the network structure and the amount of information participants have about the network. Our first result shows that information about the network structure matters and in particular affects the share of correct guesses in the network. This is inconsistent with the widely used naive (deGroot) model. The naive model is, however, consistent with a larger share of individual decisions than the competing Bayesian model, whereas both models correctly predict only about 25%–30% of consensus beliefs. We then estimate a larger class of models and find that participants do indeed take network structure into account when updating beliefs. In particular they discount information from neighbors if it is correlated, but in a more rudimentary way than a Bayesian learner would.

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7

PENG, YUN, SHENYONG ZHANG, and RONG PAN. "BAYESIAN NETWORK REASONING WITH UNCERTAIN EVIDENCES." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 18, no. 05 (October 2010): 539–64. http://dx.doi.org/10.1142/s0218488510006696.

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This paper investigates the problem of belief update in Bayesian networks (BN) with uncertain evidence. Two types of uncertain evidences are identified: virtual evidence (reflecting the uncertainty one has about a reported observation) and soft evidence (reflecting the uncertainty of an event one observes). Each of the two types of evidence has its own characteristics and obeys a belief update rule that is different from hard evidence, and different from each other. The particular emphasis is on belief update with multiple uncertain evidences. Efficient algorithms for BN reasoning with consistent and inconsistent uncertain evidences are developed, and their convergences analyzed. These algorithms can be seen as combining the techniques of traditional BN reasoning, Pearl's virtual evidence method, Jeffrey's rule, and the iterative proportional fitting procedure.

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8

MacGilchrist, Renaud S., and Julia Roloff. "A Bayesian Belief Network Exploring CSP Relationships." Academy of Management Proceedings 2015, no. 1 (January 2015): 16323. http://dx.doi.org/10.5465/ambpp.2015.16323abstract.

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9

Chaudhari, Santosh Kumar, and Hema A. Murthy. "ENERGY AWARE NETWORK: BAYESIAN BELIEF NETWORKS BASED DECISION MANAGEMENT SYSTEM." ICTACT Journal on Communication Technology 02, no. 02 (June 1, 2011): 357–62. http://dx.doi.org/10.21917/ijct.2011.0049.

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10

Xiang, Y., Y. Tang, and W. Zhu. "Mobile sensor network noise reduction and recalibration using a Bayesian network." Atmospheric Measurement Techniques 9, no. 2 (February 4, 2016): 347–57. http://dx.doi.org/10.5194/amt-9-347-2016.

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Abstract. People are becoming increasingly interested in mobile air quality sensor network applications. By eliminating the inaccuracies caused by spatial and temporal heterogeneity of pollutant distributions, this method shows great potential for atmospheric research. However, systems based on low-cost air quality sensors often suffer from sensor noise and drift. For the sensing systems to operate stably and reliably in real-world applications, those problems must be addressed. In this work, we exploit the correlation of different types of sensors caused by cross sensitivity to help identify and correct the outlier readings. By employing a Bayesian network based system, we are able to recover the erroneous readings and recalibrate the drifted sensors simultaneously. Our method improves upon the state-of-art Bayesian belief network techniques by incorporating the virtual evidence and adjusting the sensor calibration functions recursively.Specifically, we have (1) designed a system based on the Bayesian belief network to detect and recover the abnormal readings, (2) developed methods to update the sensor calibration functions infield without requirement of ground truth, and (3) extended the Bayesian network with virtual evidence for infield sensor recalibration. To validate our technique, we have tested our technique with metal oxide sensors measuring NO2, CO, and O3 in a real-world deployment. Compared with the existing Bayesian belief network techniques, results based on our experiment setup demonstrate that our system can reduce error by 34.1 % and recover 4 times more data on average.

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11

Eapen, BellRaj. "Malignancy in dermatomyositis: A Bayesian Belief Network approach." Indian Journal of Dermatology, Venereology and Leprology 73, no. 6 (2007): 445. http://dx.doi.org/10.4103/0378-6323.37080.

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12

Attoh-Okine, N. O., and S. Bowers. "A Bayesian belief network model of bridge deterioration." Proceedings of the Institution of Civil Engineers - Bridge Engineering 159, no. 2 (June 2006): 69–76. http://dx.doi.org/10.1680/bren.2006.159.2.69.

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13

Ardiansyah, Darfian, Wildan Suharso, and Gita Indah Marthasari. "Analisis Penerima Bantuan Sosial menggunakan Bayesian Belief Network." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 2, no. 2 (June 23, 2018): 506–13. http://dx.doi.org/10.29207/resti.v2i2.447.

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Bantuan Sosial merupakan pengeluaran berupa uang, barang, atau jasa yang diberikan oleh pemerintah pusat atau daerah kepada masyarakat guna melindungi masyarakat dari kemungkinan terjadinya risiko sosial, meningkatkan kemampuan ekonomi, serta kesejahteraan masyarakat. Pada kesejahteraan masyarakat, terdapat masalah yang masih belum terselesaikan sampai saat ini, yaitu kemiskinan. Kemiskinan merupakan masalah sosial yang masih belum terselesaikan di negara-negara berkembang termasuk diantaranya adalah Indonesia. Pada kemiskinan sendiri, seseorang dinyatakan miskin apabila pendapatanya lebih rendah dari garis kemiskinan serta tidak bisa untuk memenuhi kebutuhan sehari-hari. Dari permasalahan tersebut maka diperlukan analisis lebih lanjut untuk mencari kriteria yang paling berpengaruh yang nantinya dapat digunakan untuk memaksimalkan program yang telah dibuat agar taraf ekonomi pada rumah tangga sasaran dapat bertambah. Penelitian ini dilakukan untuk menganalisis faktor yang mempengaruhi perubahan taraf ekonomi menggunakan metode Bayesian Belief Network. Dari skenario yang telah dilakukan pada pengujian menggunakan metode Bayesian Belief Network ditemukan bahwa peningkatan pada kesejahteraan dan tabungan pada masyarakat di desa Srigading mampu meningkatkan perubahan taraf ekonomi hingga 71%.

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14

Kolekar, Maheshkumar H. "Bayesian belief network based broadcast sports video indexing." Multimedia Tools and Applications 54, no. 1 (July 9, 2010): 27–54. http://dx.doi.org/10.1007/s11042-010-0544-9.

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15

Amadin, F. I., and M. E. Bello. "A Bayesian Belief Network approach for predicting kernicterus." Nigerian Journal of Technology 38, no. 2 (April 17, 2019): 416. http://dx.doi.org/10.4314/njt.v38i2.18.

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16

Li, Wei, and Xu Tan. "Locally Bayesian learning in networks." Theoretical Economics 15, no. 1 (2020): 239–78. http://dx.doi.org/10.3982/te3273.

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Agents in a network want to learn the true state of the world from their own signals and their neighbors' reports. Agents know only their local networks, consisting of their neighbors and the links among them. Every agent is Bayesian with the (possibly misspecified) prior belief that her local network is the entire network. We present a tractable learning rule to implement such locally Bayesian learning: each agent extracts new information using the full history of observed reports in her local network. Despite their limited network knowledge, agents learn correctly when the network is a social quilt, a tree‐like union of cliques. But they fail to learn when a network contains interlinked circles (echo chambers), despite an arbitrarily large number of correct signals.

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17

Mohammed, Aliyu, Haitham A. Jamil, Sulaiman Mohd Nor, and Muhammad NadzirMarsono. "Malware Risk Analysis on the Campus Network with Bayesian Belief Network." International Journal of Network Security & Its Applications 5, no. 4 (July 31, 2013): 115–28. http://dx.doi.org/10.5121/ijnsa.2013.5409.

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18

Mittelstadt, Daniel, Robert Paasch, and Bruce D’Ambrosio. "Application of a Bayesian network to integrated circuit tester diagnosis." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 9, no. 1 (January 1995): 51–65. http://dx.doi.org/10.1017/s0890060400002080.

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AbstractResearch efforts to implement a Bayesian belief-network-based expert system to solve a real-world diagnostic problem – the diagnosis of integrated circuit (IC) testing machines – are described. The development of several models of the IC tester diagnostic problem in belief networks also is described, the implementation of one of these models using symbolic probabilistic inference (SPI) is outlined, and the difficulties and advantages encountered are discussed. It was observed that modeling with interdependencies in belief networks simplifies the knowledge engineering task for the IC tester diagnosis problem, by avoiding procedural knowledge and focusing on the diagnostic component’s interdependencies. Several general model frameworks evolved through knowledge engineering to capture diagnostic expertise that facilitated expanding and modifying the networks. However, model implementation was restricted to a small portion of the modeling, that of contact resistance failures, which were due to time limitations and inefficiencies in the prototype inference software we used. Further research is recommended to refine existing methods, in order to speed evaluation of the models created in this research. With this accomplished, a more complete diagnosis can be achieved.

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19

Zhang, Jian Guang, Yong Xia Li, and Ping Chen. "SAR Image Segmentation Based on Bayesian Network." Advanced Materials Research 756-759 (September 2013): 1835–39. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1835.

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In this paper, we propose a Bayesian network model. Firstly, the Bayesian network model is introduced, and Belief Propagation (BP) algorithm is utilized for model estimation. Then ExpectationMaximization (EM) algorithm is used for parameter estimation of the Bayesian network. Finally, the SAR image is segmented by calculating the Maximum Posteriori Probability (MAP) of each pixel. Experimental results show that, comparing with the Markov Random Field - Intersecting Cortical Model (MRF-ICM), our Bayesian network model gives better results in both segmentation and time-consuming.

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20

Holm, Christina E., Clare F. Grazal, Mathias Raedkjaer, Thomas Baad-Hansen, Rajpal Nandra, Robert Grimer, Jonathan A. Forsberg, Michael Moerk Petersen, and Michala Skovlund Soerensen. "Development and comparison of 1-year survival models in patients with primary bone sarcomas: External validation of a Bayesian belief network model and creation and external validation of a new gradient boosting machine model." SAGE Open Medicine 10 (January 2022): 205031212210763. http://dx.doi.org/10.1177/20503121221076387.

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Background: Bone sarcomas often present late with advanced stage at diagnosis and an according, varying short-term survival. In 2016, Nandra et al. generated a Bayesian belief network model for 1-year survival in patients with bone sarcomas. The purpose of this study is: (1) to externally validate the prior 1-year Bayesian belief network prediction model for survival in patients with bone sarcomas and (2) to develop a gradient boosting machine model using Nandra et al.’s cohort and evaluate whether the gradient boosting machine model outperforms the Bayesian belief network model when externally validated in an independent Danish population cohort. Material and Methods: The training cohort comprised 3493 patients newly diagnosed with bone sarcoma from the institutional prospectively maintained database at the Royal Orthopaedic Hospital, Birmingham, UK. The validation cohort comprised 771 patients with newly diagnosed bone sarcoma included from the Danish Sarcoma Registry during January 1, 2000–June 22, 2016. We performed area under receiver operator characteristic curve analysis, Brier score and decision curve analysis to evaluate the predictive performance of the models. Results: External validation of the Bayesian belief network 1-year prediction model demonstrated an area under receiver operator characteristic curve of 68% (95% confidence interval, 62%-73%). Area under receiver operator characteristic curve of the gradient boosting machine model demonstrated: 75% (95% confidence interval: 70%-80%), overall model performance by the Brier score was 0.09 (95% confidence interval: 0.077–0.11) and decision curve analysis demonstrated a positive net benefit for threshold probabilities above 0.5. External validation of the developed gradient boosting machine model demonstrated an area under receiver operator characteristic curve of 63% (95% confidence interval: 57%-68%), and the Brier score was 0.14 (95% confidence interval: 0.12–0.16). Conclusion: External validation of the 1-year Bayesian belief network survival model yielded a poor outcome based on a Danish population cohort validation. We successfully developed a gradient boosting machine 1-year survival model. The gradient boosting machine did not outperform the Bayesian belief network model based on external validation in a Danish population-based cohort.

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21

Paredes, Samantha, Sean Pascoe, Louisa Coglan, and Carol Richards. "Increasing Local Fish Consumption: A Bayesian Belief Network Analysis." Journal of International Food & Agribusiness Marketing 33, no. 1 (January 1, 2021): 104–21. http://dx.doi.org/10.1080/08974438.2020.1860853.

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22

Gagarin, Yu E., U. V. Nikitenko, and M. A. Stepovich. "Interval estimation of conditional probabilities in Bayesian Belief Network." Journal of Physics: Conference Series 1902, no. 1 (May 1, 2021): 012106. http://dx.doi.org/10.1088/1742-6596/1902/1/012106.

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23

Kabir, Golam, Razia Sultana Sumi, Rehan Sadiq, and Solomon Tesfamariam. "Performance evaluation of employees using Bayesian belief network model." International Journal of Management Science and Engineering Management 13, no. 2 (May 12, 2017): 91–99. http://dx.doi.org/10.1080/17509653.2017.1312583.

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24

Montironi, R., W. F. Whimster, Y. Collan, P. W. Hamilton, D. Thompson, and P. H. Bartels. "How to develop and use a Bayesian Belief Network." Journal of Clinical Pathology 49, no. 3 (March 1, 1996): 194–201. http://dx.doi.org/10.1136/jcp.49.3.194.

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25

Liao, Yi Ting, Chung-Lin Huang, and Shih-Chung Hsu. "Slip and fall event detection using Bayesian Belief Network." Pattern Recognition 45, no. 1 (January 2012): 24–32. http://dx.doi.org/10.1016/j.patcog.2011.04.017.

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26

Gan, Hongxiao, Yang Zhang, and Qun Song. "Bayesian belief network for positive unlabeled learning with uncertainty." Pattern Recognition Letters 90 (April 2017): 28–35. http://dx.doi.org/10.1016/j.patrec.2017.03.007.

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27

Clarke, Ellis. "P31 A bayesian belief network for exploratory longitudinal analysis." Controlled Clinical Trials 16, no. 3 (June 1995): 89S—90S. http://dx.doi.org/10.1016/0197-2456(95)90511-3.

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28

Lauría, Eitel J. M., and Peter J. Duchessi. "A Bayesian Belief Network for IT implementation decision support." Decision Support Systems 42, no. 3 (December 2006): 1573–88. http://dx.doi.org/10.1016/j.dss.2006.01.003.

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29

Wee, Yit Yin, Shing Chiang Tan, and KuokKwee Wee. "Reducing the Complexity of Casual Representation in Bayesian Belief Network." F1000Research 10 (December 6, 2021): 1243. http://dx.doi.org/10.12688/f1000research.73480.1.

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Background: Bayesian Belief Network (BBN) is a well-established causal framework that is widely adopted in various domains and has a proven track record of success in research and application areas. However, BBN has weaknesses in causal knowledge elicitation and representation. The representation of the joint probability distribution in the Conditional Probability Table (CPT) has increased the complexity and difficulty for the user either in comprehending the causal knowledge or using it as a front-end modelling tool. Methods: This study aims to propose a simplified version of the BBN ─ Bayesian causal model, which can represent the BBN intuitively and proposes an inference method based on the simplified version of BBN. The CPT in the BBN is replaced with the causal weight in the range of[-1,+1] to indicate the causal influence between the nodes. In addition, an inferential algorithm is proposed to compute and propagate the influence in the causal model. Results: A case study is used to validate the proposed inferential algorithm. The results show that a Bayesian causal model is able to predict and diagnose the increment and decrement as in BBN. Conclusions: The Bayesian causal model that serves as a simplified version of BBN has shown its advantages in modelling and representation, especially from the knowledge engineering perspective.

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30

Xiang, Y., Y. Tang, and W. Zhu. "Mobile sensor network noise reduction and re-calibration using Bayesian network." Atmospheric Measurement Techniques Discussions 8, no. 8 (August 31, 2015): 8971–9008. http://dx.doi.org/10.5194/amtd-8-8971-2015.

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Abstract. People are becoming increasingly interested in mobile air quality sensor network applications. By eliminating the inaccuracies caused by spatial and temporal heterogeneity of pollutant distributions, this method shows great potentials in atmosphere researches. However, such system usually suffers from the problem of sensor noises and drift. For the sensing systems to operate stably and reliably in the real-world applications, those problems must be addressed. In this work, we exploit the correlation of different types of sensors caused by cross sensitivity to help identify and correct the outlier readings. By employing a Bayesian network based system, we are able to recover the erroneous readings and re-calibrate the drifted sensors simultaneously. Specifically, we have (1) designed a Bayesian belief network based system to detect and recover the abnormal readings; (2) developed methods to update the sensor calibration functions in-field without requirement of ground truth; and (3) deployed a real-world mobile sensor network using the custom-built M-Pods to verify our assumptions and technique. Compared with the existing Bayesian belief network technique, the experiment results on the real-world data demonstrate that our system can reduce error by 34.1 % and recover 4 times more data on average.

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31

Attoh-Okine, Nii O. "Probabilistic analysis of factors affecting highway construction costs: a belief network approach." Canadian Journal of Civil Engineering 29, no. 3 (June 1, 2002): 369–74. http://dx.doi.org/10.1139/l02-003.

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This paper presents the application of belief networks to make inferences in highway construction costs. The methodology is evolving; it works very well when sufficient information and incomplete quantitative data are available. It is an attempt to identify the extent of influence of selected variables on highway construction costs. Belief networks are an expressive graphical language for representing uncertain knowledge about causal and associational relations among construction cost variables. This then provides a graphical representation of probabilistic construction cost models. The graph-theoretic framework of the belief lends itself for modeling probabilistic dependence and flow information between different construction costs and related variables in overall highway construction cost determination.Key words: construction costs, belief networks, graphical models, uncertainty, and Bayesian network.

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32

Walton, Adrian, and Del Meidinger. "Capturing expert knowledge for ecosystem mapping using Bayesian networks." Canadian Journal of Forest Research 36, no. 12 (December 1, 2006): 3087–103. http://dx.doi.org/10.1139/x06-106.

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Large-scale ecosystem maps are essential tools for managers of forest-related activities. In British Columbia, the prevailing approach for ecosystem mapping has been to use an expert system that captures expert knowledge in the form of a belief matrix. In this project, a Bayesian network rather than a belief matrix was used in an attempt to overcome some of the drawbacks of the belief-matrix approach. A Bayesian-network knowledge base was created for each of the following three biogeoclimatic variants: montane very wet maritime coastal western hemlock (CWHvm2), submontane very wet maritime coastal western hemlock (CWHvm1), and central very wet hypermaritime coastal western hemlock (CWHvh2), and applied to a study area encompassing Prince Rupert. A map of ecosystems by grouping site series was produced using each of the knowledge bases. Accuracy assessments performed on each of the maps of grouped site series revealed that the maps poorly predicted the spatial distribution of uncommon and very wet site-series groups. For example, overall map accuracy for the CWHvm2, CWHvm1, and CWHvh2 variants was 47.8%, 50.3%, and 33.3%, respectively. The results of the map-accuracy assessment, however, were consistent with those resulting from a belief-matrix approach conducted in an earlier study. We feel that Bayesian network knowledge bases are easier to develop, interpret, and update than belief matrices.

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33

Vilizzi, L., A. Price, L. Beesley, B. Gawne, A. J. King, J. D. Koehn, S. N. Meredith, D. L. Nielsen, and C. P. Sharpe. "The belief index: An empirical measure for evaluating outcomes in Bayesian belief network modelling." Ecological Modelling 228 (March 2012): 123–29. http://dx.doi.org/10.1016/j.ecolmodel.2012.01.005.

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34

Rabbi, Md, Syed Mithun Ali, Golam Kabir, Zuhayer Mahtab, and Sanjoy Kumar Paul. "Green Supply Chain Performance Prediction Using a Bayesian Belief Network." Sustainability 12, no. 3 (February 4, 2020): 1101. http://dx.doi.org/10.3390/su12031101.

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Green supply chain management (GSCM) has emerged as an important issue to lessen the impact of supply chain activities on the natural environment, as well as reduce waste and achieve sustainable growth of a company. To understand the effectiveness of GSCM, performance measurement of GSCM is a must. Monitoring and predicting green supply chain performance can result in improved decision-making capability for managers and decision-makers to achieve sustainable competitive advantage. This paper identifies and analyzes various green supply chain performance measures and indicators. A probabilistic model is proposed based on a Bayesian belief network (BBN) for predicting green supply chain performance. Eleven green supply chain performance indicators and two green supply chain performance measures are identified through an extensive literature review. Using a real-world case study of a manufacturing industry, the methodology of this model is illustrated. Sensitivity analysis is also performed to examine the relative sensitivity of green supply chain performance to each of the performance indicators. The outcome of this research is expected to help managers and practitioners of GSCM improve their decision-making capability, which ultimately results in improved overall organizational performance.

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35

Valtman, Nataliya Alexandrona, and Alexander Lvovich Tulupyev. "A Bayesian belief network directed cycle with multinomial random variables." SPIIRAS Proceedings 3, no. 14 (March 17, 2014): 170. http://dx.doi.org/10.15622/sp.14.10.

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36

Ibrahimovic, Semir, and Nijaz Bajgoric. "Modeling Information System Availability by using Bayesian Belief Network Approach." Interdisciplinary Description of Complex Systems 14, no. 2 (2016): 125–38. http://dx.doi.org/10.7906/indecs.14.2.2.

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37

Suvorova, A. V., and A. L. Tulupyev. "Bayesian Belief Network Structure Synthesis for Risky Behavior Rate Estimation." Informatsionno-upravliaiushchie sistemy (Information and Control Systems) 1, no. 92 (March 2018): 116–22. http://dx.doi.org/10.15217/issn1684-8853.2018.1.116.

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38

Daniel, Ben K., and Richard A. Schwier. "A Bayesian belief network model of a virtual learning community." International Journal of Web Based Communities 3, no. 2 (2007): 151. http://dx.doi.org/10.1504/ijwbc.2007.014077.

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39

Gagarin, Yu E., U. V. Nikitenko, and M. A. Stepovich. "Considering information uncertainty when assessing risk in Bayesian Belief Network." Journal of Physics: Conference Series 1479 (March 2020): 012054. http://dx.doi.org/10.1088/1742-6596/1479/1/012054.

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Abdo, Ammar, Valérie Leclère, Philippe Jacques, Naomie Salim, and Maude Pupin. "Prediction of New Bioactive Molecules using a Bayesian Belief Network." Journal of Chemical Information and Modeling 54, no. 1 (January 15, 2014): 30–36. http://dx.doi.org/10.1021/ci4004909.

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Lee, Eunchang, Yongtae Park, and Jong Gye Shin. "Large engineering project risk management using a Bayesian belief network." Expert Systems with Applications 36, no. 3 (April 2009): 5880–87. http://dx.doi.org/10.1016/j.eswa.2008.07.057.

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42

Zare, Fatemeh, Hasan Khademi Zare, and Mohammad Saber Fallahnezhad. "Software effort estimation based on the optimal Bayesian belief network." Applied Soft Computing 49 (December 2016): 968–80. http://dx.doi.org/10.1016/j.asoc.2016.08.004.

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Luo, Lan, Limao Zhang, and Guangdong Wu. "BAYESIAN BELIEF NETWORK-BASED PROJECT COMPLEXITY MEASUREMENT CONSIDERING CAUSAL RELATIONSHIPS." JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 26, no. 2 (February 21, 2020): 200–215. http://dx.doi.org/10.3846/jcem.2020.11930.

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This research proposes a Bayesian belief network-based approach to measure the project complexity in the construction industry. Firstly, project complexity nodes are identified for model development based on the literature review. Secondly, the project complexity measurement model is developed with 225 training samples and validated with 20 test samples. Thirdly, the developed measurement model is utilized to conduct model analytics for sequential decision making, which includes predictive, diagnostic, sensitivity, and influence chain analysis. Finally, EXPO 2010 is used to testify the effectiveness and applicability of the proposed approach. Results indicate that (1) more attention should be paid on technological complexity, information complexity, and task complexity in the process of complexity management; (2) the proposed measurement model can be applied into practice to predict the complexity level for a specific project. The uniqueness of this study lies in developing project complexity measurement model (PCMM) with the cause-effect relationships taken into account. This research contributes to (a) the state of knowledge by proposing a method that is capable of measuring the complexity level under what-if scenarios for complexity management, and (b) the state of practice by providing insights into a better understanding of causal relationships among influencing factors of complexity in construction projects.

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Shrivastava, Vivek, and R. B. Misra. "Development of Bayesian belief network model for electrical load demand." International Journal of Systems Assurance Engineering and Management 1, no. 2 (June 2010): 170–77. http://dx.doi.org/10.1007/s13198-010-0015-8.

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Kim, Keonhee, Chaehong Park, Seung-hee Kim, Doo-Hee Won, Kyung-Lak Lee, and Jiyoung Jeon. "Ecological Network on Benthic Diatom in Estuary Environment by Bayesian Belief Network Modelling." Korean Journal of Ecology and Environment 55, no. 1 (March 30, 2022): 60–75. http://dx.doi.org/10.11614/ksl.2022.55.1.060.

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Young, William A., David F. Millie, Gary R. Weckman, Jerone S. Anderson, David M. Klarer, and Gary L. Fahnenstiel. "Modeling net ecosystem metabolism with an artificial neural network and Bayesian belief network." Environmental Modelling & Software 26, no. 10 (October 2011): 1199–210. http://dx.doi.org/10.1016/j.envsoft.2011.04.004.

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Yinka-Banjo, Chika O., Isaac O. Osunmakinde, and Antoine Bagula. "Collision Avoidance in Unstructured Environments for Autonomous Robots: A Behavioural Modelling Approach." Advanced Materials Research 403-408 (November 2011): 3559–69. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.3559.

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Collision avoidance is one of the important safety key operations that needs attention in the navigation system of an autonomous robot. In this paper, a Behavioural Bayesian Network approach is proposed as a collision avoidance strategy for autonomous robots in an unstructured environment with static obstacles. In our approach, an unstructured environment was simulated and the information of the obstacles generated was used to build the Behavioural Bayesian Network Model (BBNM). This model captures uncertainties from the unstructured environment in terms of probabilities, and allows reasoning with the probabilities. This reasoning ability enables autonomous robots to navigate in any unstructured environment with a higher degree of belief that there will be no collision with obstacles. Experimental evaluations of the BBNM show that when the robot navigates in the same unstructured environment where knowledge of the obstacles is captured, there is certainty in the degree of belief that the robot can navigate freely without any collision. When the same model was tested for navigation in a new unstructured environment with uncertainties, the results showed a higher assurance or degrees of belief that the robot will not collide with obstacles. The results of our modelling approach show that Bayesian Networks (BNs) have good potential for guiding the behaviour of robots when avoiding obstacles in any unstructured environment.

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Ruggieri, Andrea, Francesco Stranieri, Fabio Stella, and Marco Scutari. "Hard and Soft EM in Bayesian Network Learning from Incomplete Data." Algorithms 13, no. 12 (December 9, 2020): 329. http://dx.doi.org/10.3390/a13120329.

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Incomplete data are a common feature in many domains, from clinical trials to industrial applications. Bayesian networks (BNs) are often used in these domains because of their graphical and causal interpretations. BN parameter learning from incomplete data is usually implemented with the Expectation-Maximisation algorithm (EM), which computes the relevant sufficient statistics (“soft EM”) using belief propagation. Similarly, the Structural Expectation-Maximisation algorithm (Structural EM) learns the network structure of the BN from those sufficient statistics using algorithms designed for complete data. However, practical implementations of parameter and structure learning often impute missing data (“hard EM”) to compute sufficient statistics instead of using belief propagation, for both ease of implementation and computational speed. In this paper, we investigate the question: what is the impact of using imputation instead of belief propagation on the quality of the resulting BNs? From a simulation study using synthetic data and reference BNs, we find that it is possible to recommend one approach over the other in several scenarios based on the characteristics of the data. We then use this information to build a simple decision tree to guide practitioners in choosing the EM algorithm best suited to their problem.

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Stefanini, Federico M. "Chain Graph Models to Elicit the Structure of a Bayesian Network." Scientific World Journal 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/749150.

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Bayesian networks are possibly the most successful graphical models to build decision support systems. Building the structure of large networks is still a challenging task, but Bayesian methods are particularly suited to exploit experts’ degree of belief in a quantitative way while learning the network structure from data. In this paper details are provided about how to build a prior distribution on the space of network structures by eliciting a chain graph model on structural reference features. Several structural features expected to be often useful during the elicitation are described. The statistical background needed to effectively use this approach is summarized, and some potential pitfalls are illustrated. Finally, a few seminal contributions from the literature are reformulated in terms of structural features.

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Spiegler, Ran. "Bayesian Networks and Boundedly Rational Expectations *." Quarterly Journal of Economics 131, no. 3 (March 7, 2016): 1243–90. http://dx.doi.org/10.1093/qje/qjw011.

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AbstractI present a framework for analyzing decision making under imperfect understanding of correlation structures and causal relations. A decision maker (DM) faces an objective long-run probability distribution p over several variables (including the action taken by previous DMs). The DM is characterized by a subjective causal model, represented by a directed acyclic graph over the set of variable labels. The DM attempts to fit this model to p , resulting in a subjective belief that distorts p by factorizing it according to the graph via the standard Bayesian network formula. As a result of this belief distortion, the DM’s evaluation of actions can vary with their long-run frequencies. Accordingly, I define a ”personal equilibrium” notion of individual behavior. The framework enables simple graphical representations of causal-attribution errors (such as coarseness or reverse causation), and provides tools for checking rationality properties of the DM’s behavior. I demonstrate the framework’s scope of applications with examples covering diverse areas, from demand for education to public policy.

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