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Learning Bayesian Networks with the bnlearn R Package

Citations

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Cited by:

  1. Prabal Das & D. A. Sachindra & Kironmala Chanda, 2022. "Machine Learning-Based Rainfall Forecasting with Multiple Non-Linear Feature Selection Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 6043-6071, December.
  2. Vuong, Quan-Hoang & La, Viet-Phuong, 2019. "The bayesvl R package. User guide v0.8.1," OSF Preprints w5dx6, Center for Open Science.
  3. Ballester-Ripoll, Rafael & Leonelli, Manuele, 2022. "Computing Sobol indices in probabilistic graphical models," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
  4. Darío Ramos-López & Ana D. Maldonado, 2021. "Cost-Sensitive Variable Selection for Multi-Class Imbalanced Datasets Using Bayesian Networks," Mathematics, MDPI, vol. 9(2), pages 1-15, January.
  5. Gallardo, Mauricio, 2022. "Measuring vulnerability to multidimensional poverty with Bayesian network classifiers," Economic Analysis and Policy, Elsevier, vol. 73(C), pages 492-512.
  6. Nikolaos M. R. Lykoskoufis & Evarist Planet & Halit Ongen & Didier Trono & Emmanouil T. Dermitzakis, 2024. "Transposable elements mediate genetic effects altering the expression of nearby genes in colorectal cancer," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
  7. Bruce G. Marcot & Anca M. Hanea, 2021. "What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis?," Computational Statistics, Springer, vol. 36(3), pages 2009-2031, September.
  8. F. Cugnata & G. Perucca & S. Salini, 2017. "Bayesian networks and the assessment of universities' value added," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(10), pages 1785-1806, July.
  9. Liman Harou, Issoufou & Whitney, Cory & Kung'u, James & Luedeling, Eike, 2021. "Crop modelling in data-poor environments – A knowledge-informed probabilistic approach to appreciate risks and uncertainties in flood-based farming systems," Agricultural Systems, Elsevier, vol. 187(C).
  10. Paula Ianishi & Oilson Alberto Gonzatto Junior & Marcos Jardel Henriques & Diego Carvalho do Nascimento & Gabriel Kamada Mattar & Pedro Luiz Ramos & Anderson Ara & Francisco Louzada, 2022. "Probability on Graphical Structure: A Knowledge-Based Agricultural Case," Annals of Data Science, Springer, vol. 9(2), pages 327-345, April.
  11. Cornwell, Nikki & Bilson, Christopher & Gepp, Adrian & Stern, Steven & Vanstone, Bruce J., 2023. "Modernising operational risk management in financial institutions via data-driven causal factors analysis: A pre-registered report," Pacific-Basin Finance Journal, Elsevier, vol. 77(C).
  12. Quan-Hoang Vuong & Manh-Tung Ho & Hong-Kong T. Nguyen & Thu-Trang Vuong & Trung Tran & Khanh-Linh Hoang & Thi-Hanh Vu & Phuong-Hanh Hoang & Minh-Hoang Nguyen & Manh-Toan Ho & Viet-Phuong La, 2020. "On how religions could accidentally incite lies and violence: folktales as a cultural transmitter," Palgrave Communications, Palgrave Macmillan, vol. 6(1), pages 1-13, December.
  13. Kathrin Plankensteiner & Olivia Bluder & Jürgen Pilz, 2015. "Bayesian Network Model with Application to Smart Power Semiconductor Lifetime Data," Risk Analysis, John Wiley & Sons, vol. 35(9), pages 1623-1639, September.
  14. Ryan G. Lim & Osama Al-Dalahmah & Jie Wu & Maxwell P. Gold & Jack C. Reidling & Guomei Tang & Miriam Adam & David K. Dansu & Hye-Jin Park & Patrizia Casaccia & Ricardo Miramontes & Andrea M. Reyes-Ort, 2022. "Huntington disease oligodendrocyte maturation deficits revealed by single-nucleus RNAseq are rescued by thiamine-biotin supplementation," Nature Communications, Nature, vol. 13(1), pages 1-23, December.
  15. Roland R. Ramsahai, 2020. "Connecting actuarial judgment to probabilistic learning techniques with graph theory," Papers 2007.15475, arXiv.org.
  16. Leszek Chomacki & Janusz Rusek & Leszek Słowik, 2022. "Machine Learning Methods in Damage Prediction of Masonry Development Exposed to the Industrial Environment of Mines," Energies, MDPI, vol. 15(11), pages 1-23, May.
  17. Tang, Kayu & Parsons, David J. & Jude, Simon, 2019. "Comparison of automatic and guided learning for Bayesian networks to analyse pipe failures in the water distribution system," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 24-36.
  18. Robert Stojnic & Audrey Qiuyan Fu & Boris Adryan, 2012. "A Graphical Modelling Approach to the Dissection of Highly Correlated Transcription Factor Binding Site Profiles," PLOS Computational Biology, Public Library of Science, vol. 8(11), pages 1-13, November.
  19. Scutari, Marco, 2017. "Bayesian Network Constraint-Based Structure Learning Algorithms: Parallel and Optimized Implementations in the bnlearn R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i02).
  20. Azzimonti, Laura & Corani, Giorgio & Zaffalon, Marco, 2019. "Hierarchical estimation of parameters in Bayesian networks," Computational Statistics & Data Analysis, Elsevier, vol. 137(C), pages 67-91.
  21. Myriam Patricia Cifuentes & Clara Mercedes Suarez & Ricardo Cifuentes & Noel Malod-Dognin & Sam Windels & Jose Fernando Valderrama & Paul D. Juarez & R. Burciaga Valdez & Cynthia Colen & Charles Phill, 2022. "Big Data to Knowledge Analytics Reveals the Zika Virus Epidemic as Only One of Multiple Factors Contributing to a Year-Over-Year 28-Fold Increase in Microcephaly Incidence," IJERPH, MDPI, vol. 19(15), pages 1-21, July.
  22. Amanda M. Y. Chu & Lupe S. H. Chan & Stephen S. Y. Chang & Agnes Tiwari & Helina Yuk & Mike K. P. So, 2024. "Applications of Bayesian networks in assessing the effects of family resilience on caregiver behavioral problems, depressive symptoms, and burdens," Journal of Computational Social Science, Springer, vol. 7(2), pages 1275-1303, October.
  23. Babak Fazelabdolabadi, 2019. "A hybrid Bayesian-network proposition for forecasting the crude oil price," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-21, December.
  24. Yi Tan & Prakash P. Shenoy & Ben Sherwood & Catherine Shenoy & Melinda Gaddy & Mary E. Oehlert, 2024. "Bayesian Network Models for PTSD Screening in Veterans," INFORMS Journal on Computing, INFORMS, vol. 36(2), pages 495-509, March.
  25. Michael J McGeachie & Hsun-Hsien Chang & Scott T Weiss, 2014. "CGBayesNets: Conditional Gaussian Bayesian Network Learning and Inference with Mixed Discrete and Continuous Data," PLOS Computational Biology, Public Library of Science, vol. 10(6), pages 1-7, June.
  26. Lagani, Vincenzo & Athineou, Giorgos & Farcomeni, Alessio & Tsagris, Michail & Tsamardinos, Ioannis, 2017. "Feature Selection with the R Package MXM: Discovering Statistically Equivalent Feature Subsets," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 80(i07).
  27. Ronja Foraita & Juliane Friemel & Kathrin Günther & Thomas Behrens & Jörn Bullerdiek & Rolf Nimzyk & Wolfgang Ahrens & Vanessa Didelez, 2020. "Causal discovery of gene regulation with incomplete data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1747-1775, October.
  28. Silvia de Juan & Maria Dulce Subida & Andres Ospina-Alvarez & Ainara Aguilar & Miriam Fernandez, 2020. "Disentangling the socio-ecological drivers behind illegal fishing in a small-scale fishery managed by a TURF system," Papers 2012.08970, arXiv.org.
  29. Lidia Ceriani & Chiara Gigliarano, 2020. "Multidimensional Well-Being: A Bayesian Networks Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 152(1), pages 237-263, November.
  30. Almudevar, Anthony, 2016. "An information theoretic approach to pedigree reconstruction," Theoretical Population Biology, Elsevier, vol. 107(C), pages 52-64.
  31. David J. Klinke & Audry Fernandez & Wentao Deng & Atefeh Razazan & Habibolla Latifizadeh & Anika C. Pirkey, 2022. "Data-driven learning how oncogenic gene expression locally alters heterocellular networks," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
  32. Meineri, Eric & Dahlberg, C. Johan & Hylander, Kristoffer, 2015. "Using Gaussian Bayesian Networks to disentangle direct and indirect associations between landscape physiography, environmental variables and species distribution," Ecological Modelling, Elsevier, vol. 313(C), pages 127-136.
  33. Michail Tsagris, 2021. "A New Scalable Bayesian Network Learning Algorithm with Applications to Economics," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 341-367, January.
  34. Jenny Häggström, 2018. "Data†driven confounder selection via Markov and Bayesian networks," Biometrics, The International Biometric Society, vol. 74(2), pages 389-398, June.
  35. Mauricio Gallardo & María Emma Santos & Pablo Villatoro & Vicky Pizarro, 2021. "Measuring vulnerability to multidimensional poverty in Latin América," Asociación Argentina de Economía Política: Working Papers 4476, Asociación Argentina de Economía Política.
  36. Daniel Gartner & Rainer Kolisch & Daniel B. Neill & Rema Padman, 2015. "Machine Learning Approaches for Early DRG Classification and Resource Allocation," INFORMS Journal on Computing, INFORMS, vol. 27(4), pages 718-734, November.
  37. Andrew A. Brown & Juan J. Fernandez-Tajes & Mun-gwan Hong & Caroline A. Brorsson & Robert W. Koivula & David Davtian & Théo Dupuis & Ambra Sartori & Theodora-Dafni Michalettou & Ian M. Forgie & Jonath, 2023. "Genetic analysis of blood molecular phenotypes reveals common properties in the regulatory networks affecting complex traits," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
  38. Ünsal-Altuncan, Izel & Vanhoucke, Mario, 2024. "A hybrid forecasting model to predict the duration and cost performance of projects with Bayesian Networks," European Journal of Operational Research, Elsevier, vol. 315(2), pages 511-527.
  39. Lyu, Rongfang & Zhao, Wenpeng & Pang, Jili & Tian, Xiaolei & Zhang, Jianming & Wang, Naiang, 2022. "Towards a sustainable nature reserve management: Using Bayesian network to quantify the threat of disturbance to ecosystem services," Ecosystem Services, Elsevier, vol. 58(C).
  40. Marco Scutari, 2020. "Bayesian network models for incomplete and dynamic data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(3), pages 397-419, August.
  41. Vuong, Quan-Hoang & La, Viet-Phuong & Ho, Manh-Toan, 2019. "The bayesvl R package. Hướng dẫn sử dụng v0.8," OSF Preprints yacs5, Center for Open Science.
  42. Pekka Kekolahti & Juuso Karikoski & Antti Riikonen, 2015. "The effect of an individual’s age on the perceived importance and usage intensity of communications services—A Bayesian Network analysis," Information Systems Frontiers, Springer, vol. 17(6), pages 1313-1333, December.
  43. Yi-Sheng Chao & Marco Scutari & Tai-Shen Chen & Chao-Jung Wu & Madeleine Durand & Antoine Boivin & Hsing-Chien Wu & Wei-Chih Chen, 2018. "A network perspective of engaging patients in specialist and chronic illness care: The 2014 International Health Policy Survey," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-21, August.
  44. Saideep Nannapaneni & Sankaran Mahadevan & Abhishek Dubey & Yung-Tsun Tina Lee, 2021. "Online monitoring and control of a cyber-physical manufacturing process under uncertainty," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1289-1304, June.
  45. Wang, Bingling & Zhou, Qing, 2021. "Causal network learning with non-invertible functional relationships," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).
  46. Yauheniya Cherkas & Joshua Ide & John Stekelenborg, 2022. "Leveraging Machine Learning to Facilitate Individual Case Causality Assessment of Adverse Drug Reactions," Drug Safety, Springer, vol. 45(5), pages 571-582, May.
  47. Scutari Marco & Mackay Ian & Balding David, 2013. "Improving the efficiency of genomic selection," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(4), pages 517-527, August.
  48. Xueqin Wang & Wenliang Pan & Wenhao Hu & Yuan Tian & Heping Zhang, 2015. "Conditional Distance Correlation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1726-1734, December.
  49. Rosy Oh & Hong Kyu Lee & Youngmi Kim Pak & Man-Suk Oh, 2022. "An Interactive Online App for Predicting Diabetes via Machine Learning from Environment-Polluting Chemical Exposure Data," IJERPH, MDPI, vol. 19(10), pages 1-17, May.
  50. Michael J. Brusco & Douglas Steinley & Ashley L. Watts, 2022. "Disentangling relationships in symptom networks using matrix permutation methods," Psychometrika, Springer;The Psychometric Society, vol. 87(1), pages 133-155, March.
  51. Bassamzadeh, Nastaran & Ghanem, Roger, 2017. "Multiscale stochastic prediction of electricity demand in smart grids using Bayesian networks," Applied Energy, Elsevier, vol. 193(C), pages 369-380.
  52. Babak Fazelabdolabadi, 2019. "Uncertainty and energy-sector equity returns in Iran: a Bayesian and quasi-Monte Carlo time-varying analysis," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-20, December.
  53. Toyosi Ademujimi & Vittaldas Prabhu, 2024. "Model-Driven Bayesian Network Learning for Factory-Level Fault Diagnostics and Resilience," Sustainability, MDPI, vol. 16(2), pages 1-22, January.
  54. Piotr Kosowski & Katarzyna Kosowska & Wojciech Nawalaniec, 2022. "Application of Bayesian Networks in Modeling of Underground Gas Storage Energy Security," Energies, MDPI, vol. 15(14), pages 1-24, July.
  55. Wang, Yuhong & Zhang, Fan & Yang, Zhisen & Yang, Zaili, 2021. "Incorporation of deficiency data into the analysis of the dependency and interdependency among the risk factors influencing port state control inspection," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
  56. Cornwell, Nikki & Bilson, Christopher & Gepp, Adrian & Stern, Steven & Vanstone, Bruce J., 2023. "Modernising operational risk management in financial institutions via data-driven causal factors analysis: A pre-registered study," Pacific-Basin Finance Journal, Elsevier, vol. 79(C).
  57. Sangsung Park & Sunghae Jun, 2020. "Patent Keyword Analysis of Disaster Artificial Intelligence Using Bayesian Network Modeling and Factor Analysis," Sustainability, MDPI, vol. 12(2), pages 1-11, January.
  58. Małgorzata Łazȩcka & Jan Mielniczuk, 2023. "Squared error-based shrinkage estimators of discrete probabilities and their application to variable selection," Statistical Papers, Springer, vol. 64(1), pages 41-72, February.
  59. Mandhani, Jyoti & Nayak, Jogendra Kumar & Parida, Manoranjan, 2020. "Interrelationships among service quality factors of Metro Rail Transit System: An integrated Bayesian networks and PLS-SEM approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 140(C), pages 320-336.
  60. Shuai Zhao & Yunhai Tong & Zitian Wang & Shaohua Tan, 2016. "Identifying Key Drivers of Return Reversal with Dynamical Bayesian Factor Graph," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-20, November.
  61. Michail Tsagris, 2022. "The FEDHC Bayesian Network Learning Algorithm," Mathematics, MDPI, vol. 10(15), pages 1-28, July.
  62. Hobæk Haff, Ingrid & Aas, Kjersti & Frigessi, Arnoldo & Lacal, Virginia, 2016. "Structure learning in Bayesian Networks using regular vines," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 186-208.
  63. Adrienne L. Contasti & Alexandra G. Firth & Beth H. Baker & John P. Brooks & Martin A. Locke & Dana J. Morin, 2023. "Balancing Tradeoffs in Climate-Smart Agriculture: Will Selling Carbon Credits Offset Potential Losses in the Net Yield Income of Small-Scale Soybean ( Glycine max L.) Producers in the Mid-Southern Uni," Decision Analysis, INFORMS, vol. 20(4), pages 252-275, December.
  64. Gonzalo A. Ruz & Pamela Araya-Díaz, 2018. "Predicting Facial Biotypes Using Continuous Bayesian Network Classifiers," Complexity, Hindawi, vol. 2018, pages 1-14, December.
  65. Guadagno, C.R. & Millar, D. & Lai, R. & Mackay, D.S. & Pleban, J.R. & McClung, C.R. & Weinig, C. & Wang, D.R. & Ewers, B.E., 2020. "Use of transcriptomic data to inform biophysical models via Bayesian networks," Ecological Modelling, Elsevier, vol. 429(C).
  66. Francis, Royce A. & Guikema, Seth D. & Henneman, Lucas, 2014. "Bayesian Belief Networks for predicting drinking water distribution system pipe breaks," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 1-11.
  67. María Morales & Antonio Salmerón & Ana D. Maldonado & Andrés R. Masegosa & Rafael Rumí, 2022. "An Empirical Analysis of the Impact of Continuous Assessment on the Final Exam Mark," Mathematics, MDPI, vol. 10(21), pages 1-21, October.
  68. Vuong, Quan-Hoang & La, Viet-Phuong, 2019. "Ứng dụng BayesVL v0.6.5 mô phỏng MCMC với bài toán burden ~ res + insured sử dụng dữ liệu thực 1042 quan sát," OSF Preprints 9rhyk, Center for Open Science.
  69. Ian Dinwoodie & Kruti Pandya, 2015. "Exact tests for singular network data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(4), pages 687-706, August.
  70. Federica Cugnata & Silvia Salini & Elena Siletti, 2021. "Deepening Well-Being Evaluation with Different Data Sources: A Bayesian Networks Approach," IJERPH, MDPI, vol. 18(15), pages 1-10, July.
  71. Sagnik Datta & Ghislaine Gayraud & Eric Leclerc & Frederic Y. Bois, 2017. "Graph_sampler: a simple tool for fully Bayesian analyses of DAG-models," Computational Statistics, Springer, vol. 32(2), pages 691-716, June.
  72. Minh, Man Duc Binh & Van Cuong, Dinh & Linh, Nguyen Thi Linh & Ho, Manh-Toan, 2019. "Xây dựng mô hình phát hiện gian lận trong báo cáo tài chính của các công ty tại Việt Nam," OSF Preprints kecmv, Center for Open Science.
  73. Martin Huber, 2024. "An Introduction to Causal Discovery," Papers 2407.08602, arXiv.org.
  74. Zywiec, William J. & Mazzuchi, Thomas A. & Sarkani, Shahram, 2021. "Analysis of process criticality accident risk using a metamodel-driven Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
  75. Borochin, Paul & Rush, Stephen, 2022. "Information networks in the financial sector and systemic risk," Journal of Banking & Finance, Elsevier, vol. 134(C).
  76. Paula Laccourreye & Concha Bielza & Pedro Larrañaga, 2022. "Explainable Machine Learning for Longitudinal Multi-Omic Microbiome," Mathematics, MDPI, vol. 10(12), pages 1-23, June.
  77. Bibartiu, Otto & Dürr, Frank & Rothermel, Kurt & Ottenwälder, Beate & Grau, Andreas, 2021. "Scalable k-out-of-n models for dependability analysis with Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
  78. Pedro Bonilla-Nadal & Andrés Cano & Manuel Gómez-Olmedo & Serafín Moral & Ofelia Paula Retamero, 2022. "Using Value-Based Potentials for Making Approximate Inference on Probabilistic Graphical Models," Mathematics, MDPI, vol. 10(14), pages 1-27, July.
  79. Lingfei Wang, 2021. "Single-cell normalization and association testing unifying CRISPR screen and gene co-expression analyses with Normalisr," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
  80. Bryan Keller, 2020. "Variable Selection for Causal Effect Estimation: Nonparametric Conditional Independence Testing With Random Forests," Journal of Educational and Behavioral Statistics, , vol. 45(2), pages 119-142, April.
  81. Vuong, Quan-Hoang & La, Viet-Phuong, 2019. "Mô phỏng hierarchy MCMC cho mô hình Satlns ~ end + ses + res + insured, BayesVL v0.6.5 trên 1042 quan sát thực," OSF Preprints dm467, Center for Open Science.
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