We are listing here the publications based on our software. This is by no means a complete list and it contains only those publications that we are aware of. Please help us with keeping this list up to date by letting us know about your publications.

Books:

  • Narasi Sridhar (ed.) (2024) “Bayesian Network Modeling of Corrosion”, Springer Nature Switzerland AG.

  • Bhattacharyya, S., Maulik, U., Dutta, P. (2016) “Quantum Inspired Computational Intelligence: Research and Applications”, Morgan Kaufmann.

Journals:

  • Yajie Yang, Soora Rasouli and Feixiong Liao. Integrated modeling of residential and work-related mobilities: A large-scale dynamic Bayesian network approach, Cities, Volume 156, 2025, https://doi.org/10.1016/j.cities.2024.105546.
  • Yajie Yang, Soora Rasouli and Feixiong Liao. Effects of life events and attitudes on vehicle transactions: A dynamic Bayesian network approach. Transportation Research, Part C, 147, 103988, 2023.
  • Hendrik Morgenstern and Michael Raupach. Predictive BIM with Integrated Bayesian Inference of Deterioration Models as a Four-Dimensional Decision Support Tool. Civil Engineering, 4(1):185-203, 2023. https://doi.org/10.3390/civileng4010012
  • Narasi Sridhar. Localized Corrosion in Seawater: A Bayesian Network-Based Review. Corrosion, 79(3):268-283, 2022.
  • Helen J. Mayfield, Colleen L. Lau, Jane E. Sinclair, Samuel J. Brown, Andrew Baird, John Litt, Aapeli Vuorinen, Kirsty R. Short, Michael Waller and Kerrie Mengerseng. Designing an evidence-based Bayesian network for estimating the risk versus benefits of AstraZeneca COVID-19 vaccine. Vaccine, 40(22): 3072–3084, May 11, 2022.
  • Lei Li and Zihao Fang. Cause Analysis of Coal Mine Gas Explosion Based on Bayesian Network. Shock and Vibration, vol. 2022, Article ID 1923734, 10 pages, 2022. https://doi.org/10.1155/2022/1923734
  • Nicolas Meurisse, Bruce G. Marcot, Owen Woodberry, Barbara I.P. Barratt and Jacqui H. Todd. Risk Analysis Frameworks Used in Biological Control and Introduction of a Novel Bayesian Network Tool. Risk Analysis, 42(6):1255-1276, 2022. https://doi.org/10.1111/risa.13812
  • Halil, N, Rusli, R, Zainal Abidin, M, Jamen, S, Khan, F. An integrated health risk assessment with control banding for nanomaterials exposure. Process Safety Progress, 2022; 41( S1): S84- S97. doi:10.1002/prs.12327
  • Nicholas L. Rider, Gina Cahill, Tina Motazedi, Lei Wei, Ashok Kurian, Lenora M. Noroski, Filiz O. Seeborg, Ivan K. Chinn and Kirk Roberts. PI Prob: A risk prediction and clinical guidance system for evaluating patients with recurrent infections. PLoS ONE 16(2): e0237285. https://doi.org/10.1371/
    journal.pone.0237285, 2021.
  • Nima Khakzad and Faisal Khan. Application of Bayesian network to domino effect assessment. In Valerio Cozzani, Genserik Reniers (Eds.): Dynamic Risk Assessment and Management of Domino Effects and Cascading Events in the Process Industry, Elsevier, pp.49-71, ISBN 9780081028384, 2021.
  • Nima Khakzad. Optimal firefighting to prevent domino effects: Methodologies based on dynamic influence diagram and mathematical programming. Reliability Engineering and System Safety, 212; 107577, 2021.
  • Guorong Li, Jinxian Weng and Zhiqiang Hou. Impact analysis of external factors on human errors using the ARBN method based on small-sample ship collision records. Ocean Engineering 236(2):109533. DOI: 10.1016/j.oceaneng.2021.109533
  • Floris Goerlandt and Samsul Islam. A Bayesian Network risk model for estimating coastal maritime transportation delays following an earthquake in British Columbia. Reliability Engineering and System Safety 214(10):107708, April 2021. DOI: 10.1016/j.ress.2021.107708
  • Qing Yu, Angelo Palos Teixeira, Kezhong Liu, Hao Rong and Carlos Guedes Soares. An integrated dynamic ship risk model based on Bayesian networks and evidential reasoning. Reliability Engineering and System Safety, 216, 2021. DOI: 10.1016/j.ress.2021.107993
  • Junen Wu, Huanhuan Zeng, Fan Zhao, Chunfeng Chen, Xiaojin Jiang, Xiai Zhu, Pingyuan Wang, Zhixiang Wu and Wenjie Liu. The nutrient status of plant roots reveals competition intensities in rubber agroforestry systems. Forests 11(11):1163, January 2021. DOI: 10.3390/f11111163
  • Nima Khakzad. (mis)Using Bayesian network for dynamic risk assessment. In Faisal Khan and Paul Amyotte (Eds.): Methods in Chemical Process Safety, Elsevier, Vol. 4, pp.123-165, ISBN 9780128218242, 2020.
  • Edward A. Selby, Sergiy Kondratyuk, Janne Lindqvist, Kara Fehling and Amy Kranzler (2020). Temporal Bayesian Network Modeling Approach to Evaluating the Emotional Cascade Model of Borderline Personality Disorder. Personality Disorders: Theory, Research, and Treatment, 12(1), 39–50, 2021. https://doi.org/10.1037/per0000398
  • Ana Rita Nogueira, João Gama and Carlos Abreu Ferreira. Causal discovery in machine learning: Theories and applications. Journal of Dynamics & Games 8(3), July 2021. DOI: 10.3934/jdg.2021008
  • Saung Hnin Pwint Oo, Nguyen Duy Hung and Thanaruk Theeramunkong. Knowledge integration by probabilistic argumentation. IEICE Transactions on Information and Systems E103.D(8):1843-1855, August 2020.
  • Duarte Dinis, A.P. Teixeira and Carlos Guedes Soares. Probabilistic approach for characterising the static risk of ships using Bayesian networks. Reliability Engineering and System Safety, 203(C):107073, June 2020. DOI: 10.1016/j.ress.2020.107073
  • Mario Michiels, Pedro Larranaga and Concha Bielza. BayeSuites: An open web framework for massive Bayesian networks focused on neuroscience. Neurocomputing, 428:166-181, 7 March 2021.
  • Felipe Sanchez, Eric Bonjour, Jean-Pierre Micaelli and Davy Monticolo. An Approach Based on Bayesian Network for Improving Project Management Maturity: An Application to Reduce Cost Overrun Risks in Engineering Projects. Computers in Industry 119(103227), 2020.
  • Dirk Vrebos, Arwyn Jones, Emanuele Lugato, Lilian O’Sullivan, Rogier Schulte, Jan Staes, Patrick Meire. Spatial evaluation and trade‐off analysis of soil functions through Bayesian networks. European Journal of Soil Science 72(4), August 2020. DOI: 10.1111/ejss.13039
  • Catherine Dezan, Sara Zermani and Chabha Hireche. Embedded Bayesian network contribution for a safe mission planning of autonomous vehicles. Algorithms 13(7):155, 2020. doi:10.3390/a13070155
  • Junxiang Li, Bin Dai, Xiaohui Li, Xin Xu and Daxue Liu. A dynamic Bayesian network for vehicle maneuver prediction in highway driving scenarios: Framework and verification. Electronics, 8(1):1-19, 2019.
  • Nima Khakzad. Modeling wildfire spread in wildland-industrial interfaces using dynamic Bayesian network. Reliability Engineering and System Safety, 189:165-176, 2019.
  • Leen Depauw, Dries Landuyt, Michael P.Perring, Haben Blondeel, Sybryn L. Maesa, Martin Kopecký, František Máliš, Margot Vanhellemont and KrisVerheyen. A general framework for quantifying the effects of land-use history on ecosystem dynamics. Ecological Indicators, 107, December 2019.
  • Jiajin Chen, Ruyang Zhang, Xuesi Dong, Lijuan Lin, Ying Zhu, Jieyu He, David C. Christiani, Yongyue Wei and Feng Chen. shinyBN: an online application for interactive Bayesian network inference and visualization. BMC Bioinformatics, 20: 711, 2019. https://doi.org/10.1186/s12859-019-3309-0
  • Altyngul Zinetullina, Ming Yang, Nima Khakzad and Boris Golman. Dynamic resilience assessment for process units operating in Arctic environments. Safety in Extreme Environments, 2:113-125, 2019.
  • Duygu Çelik Ertugrul and Atilla Elçi. A survey on semanticized and personalized health recommender systems. Expert Systems, e12519, 2019. https://onlinelibrary.wiley.com/doi/10.1111/exsy.12519
  • Alind Gupta, Justin J. Slater, Devon Boyne, Nicholas Mitsakakis, Audrey Béliveau, Marek J. Druzdzel, Darren R. Brenner, Selena Hussain, Paul Arora. Probabilistic graphical modeling for estimating risk of coronary artery disease: Applications of a flexible machine-learning method. Medical Decision Making 39(6), October 2019. https://doi.org/10.1177/0272989X19879095
  • Javier Galapero, Sara Fernández, Carlos J. Pérez, F. Calle-Alonso, Joaquín Rey and Luis Gómez. Exploring the importance of mixed autogenous vaccines as a potential determinant of lung consolidation in lambs using Bayesian networks. Preventive Veterinary Medicine, 169:1-7, 2019.
  • Angelos Chatzimparmpas and Stamatia Bibi. Maintenance process modeling and dynamic estimations based on Bayesian networks and association rules. Journal of Software: Evolution and Process, 31(9), March 2019. http://dx.doi.org/10.1002/smr.2163
  • Young-Suk Lee, Arjun Krishnan, Rose Oughtred, Jennifer Rust, Christie S. Chang, Joseph Ryu, Vessela N. Kristensen, Kara Dolinski, Chandra L. Theesfeld, Olga G. Troyanskaya. A Computational Framework for Genome-wide Characterization of the Human Disease Landscape. Cell Systems, 8(2):152-162, 27 February 2019. doi: 10.1016/j.cels.2018.12.010
  • Anthony Kerebel, Nancy Gélinas, Steve Déry, Brian Voigt and Alison Munson. Landscape aesthetic modelling using Bayesian networks: Conceptual framework and participatory indicator weighting. Landscape and Urban Planning, 185: 258-271, May 2019. https://doi.org/10.1016/j.landurbplan.2019.02.001
  • Wantida Horpiencharoen, Sukanya Thongratsakul and Chaithep Poolkhet. Risk factors of clinical mastitis and antimicrobial susceptibility test results of mastitis milk from dairy cattle in western Thailand: Bayesian network analysis. Preventive Veterinary Medicine, 164:49-55, February 2019.
  • Rishu Chhabra, Rama Krishna Challa and Seema Verma. Smartphone based context-Aware driver behavior classification using dynamic Bayesian network. Journal of Intelligent and Fuzzy Systems 36(5):1-14, January 2019. http://dx.doi.org/10.3233/JIFS-169995
  • Jin Lee, Robert Henning and Martin Cherniack. Correction workers’ burnout and outcomes: A Bayesian network approach. International Journal of Environmental Research and Public Health, 16(2):282, January 2019. http://dx.doi.org/10.3390/ijerph16020282

  • Jinzhi Lu, Guoxin Wang, Xin Tao, Jin Wang and Martin Törngren. A domain-specific modeling approach supporting tool-chain development with Bayesian network models. Integrated Computer Aided Engineering, 27:153-171. November 2019.

  • MCarmen Romero-Ternero, David Oviedo-Olmedo, Alejandro Carrasco and Joaquín Luque. A distributed approach for estimating battery state-of-charge in solar farms. Sensors 19(22):1-19, November 2019.

  • Jeevith Hegde, Ingrid Bouwer Utne, Ingrid Schjølberg and Brede Thorkildsen. A Bayesian approach to risk modeling of autonomous subsea intervention operations. Reliability Engineering: System Safety 175:142-159, July 2018.

  • Duarte Dinis, Ana Paula Barbosa-Povoa and A.P. Teixeira. Valuing data in aircraft maintenance through big data analytics: A probabilistic approach for capacity planning using Bayesian networks. Computers & Industrial Engineering 128:920-936, February 2019. DOI: 10.1016/j.cie.2018.10.015

  • Adrian Adam Falkowski, Anna Łupinska–Dubicka. Empirical Evaluation of Methods of Filling the Missing Data in Learning Probabilistic Models. Advances in Computer Science Research, 14:55-67, 2018.

  • Alessandro Vallero, Alessandro Savino, Athanasios Chatzidimitriou, Manolis Kaliorakis and Stefano Di Carlo. SyRA: Early System Reliability Analysis for Cross-Layer Soft Errors Resilience in Memory Arrays of Microprocessor Systems. IEEE Transactions on Computers 68(5):765-783, December 2018. http://dx.doi.org/10.1109/TC.2018.2887225
  • Alessandro Magrini, Davide Luciani and Federico M. Stefanini. A probabilistic network for the diagnosis of acute cardiopulmonary diseases.  Biometrical Journal 60(1):174-195, January 2018. http://dx.doi.org/10.1002/bimj.201600206
  • W. Joseph MacInnes, Amelia R. Hunt, Alasdair D.F. Clarke and Michael D. Dodd. A Generative Model of Cognitive State from Task and Eye Movements. Cognitive Computation 10(5):703-717, October 2018. https://link.springer.com/article/10.1007/s12559-018-9558-9
  • Nima Khakzad. Which fire to extinguish first? A risk-informed approach to emergency response in oil terminals. Risk Analysis 38(7):1444-1454, 2018.
  • Foroogh Ghasemi, Mohammad Hossein Mahmoudi Sari, Vahidreza Yousefi, Reza Falsafi, and Jolanta Tamošaitienė. Project Portfolio Risk Identification and Analysis, Considering Project Risk Interactions and Using Bayesian Networks. Sustainability 10, no. 5: 1609, 2018. https://doi.org/10.3390/su10051609
  • Nima Khakzad & Pieter van Gelder. Vulnerability of industrial plants to flood-induced natechs: A Bayesian network approach, Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 403-411, 2018.

  • Ye Ye, Michael M. Wagner, Gregory F. Cooper, Jeffrey P. Ferraro, Howard Su, Per H Gesteland, Peter J Haug, Nicholas E. Millett, John M. Aronis, Andrew J. Nowalk, Victor M. Ruiz, Arturo Lopez Pineda, Lingyun Shi, Rudy Van Bree, Thomas Ginter and Fuchiang (Rich) Tsui. A study of the transferability of influenza case detection systems between two large healthcare systems. PLoS ONE 12(4), April 2017. http://dx.doi.org/10.1371/journal.pone.0174970

  • Alberto Franzin, Francesco Sambo and Barbara Di Camillo. Bnstruct: An R package for Bayesian network structure learning in the presence of missing data. Bioinformatics, 33(8):1250–1252, 2017. https://doi.org/10.1093/bioinformatics/btw807
  • Ye Ye, Michael M. Wagner, Gregory F. Cooper, Jeffrey P. Ferraro and Fuchiang Tsui. A study of the transferability of influenza case detection systems between two large healthcare systems. PLoS ONE 12(4), April 2017. http://dx.doi.org/10.1371/journal.pone.0174970
  • Michael C. Darling, George F. Luger, Thomas B. Jones, Matthew R. Denman and Katrina M. Groth. Intelligent Modeling for Nuclear Power Plant Accident Management. International Journal of Artificial Intelligence Tools 27(2), December 2017. http://dx.doi.org/10.1142/S0218213018500033
  • Divya A. Varkey, Murdoch K. McAllister, Paul J. Askey, Eric Parkinson, Adrian Clarke and Theresa Godin. Multi-Criteria Decision Analysis for Recreational Trout Fisheries in British Columbia, Canada: A Bayesian Network Implementation. North American Journal of Fisheries Management, 36:1457–1472, 2016
  • P. Sotiralis, N.P. Ventikos, R. Hamann, P. Golyshev and A.P. Teixeira. Incorporation of human factors into ship collision risk models focusing on human centred design aspects. Reliability Engineering and System Safety 156: 210–227, 2016.
  • Arthur Jochems, Timo M. Deist, Johan van Soest, Michael Eble and Andre Dekker. Distributed learning: Developing a predictive model based on data from multiple hospitals without data leaving the hospital – A real life proof of concept. Radiotherapy and Oncology 121(3), October 2016. http://dx.doi.org/10.1016/j.radonc.2016.10.002
  • S. Gerassis, J. E. Martín, J. Taboada García, A. Saavedra and J. Taboada. Bayesian Decision Tool for the Analysis of Occupational Accidents in the Construction of Embankments. Journal of Construction Engineering and Management 143(2):04016093, August 2016. http://dx.doi.org/10.1061/(ASCE)CO.1943-7862.0001225
  • Panayiotis Petousis, Simon X. Han, Denise Aberle and Alex A.T. Bui. Prediction of lung cancer incidence on the low-dose computed tomography arm of the National Lung Screening Trial: A dynamic Bayesian network. Artificial Intelligence in Medicine, 72:42-55, September 2016. http://dx.doi.org/10.1016/j.artmed.2016.07.001
  • François-Xavier Aguessy, Olivier Bettan, Grégory Blanc, Vania Conan and Hervé Debar. Bayesian Attack Model for Dynamic Risk Assessment. CoRR, abs/1606.09042, 2016.

  • Natasha A. Loghmanpour, Robert L. Kormos, Manreet K. Kanwar, Jeffrey J. Teuteberg, Srinivas Murali and James F. Antaki. A Bayesian Model to Predict Right Ventricular Failure Following Left Ventricular Assist Device Therapy. JACC: Heart Failure 4(9). June 2016.

  • Natasha A. Loghmanpour, Robert L. Kormos, Manreet K. Kanwar, Jeffrey J. Teuteberg, Srinivas Murali and James F. Antaki. A Bayesian Model to Predict Right Ventricular Failure Following Left Ventricular Assist Device Therapy. JACC: Heart Failure 4(9). June 2016.

  • Constantinou, A. C., Marsh, W., Fenton, N. & Radlinski, L. From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support. Artificial Intelligence in Medicine, 67: 75-93, 2016. doi:10.1016/j.artmed.2016.01.002

  • Rowena Cave and D. Kimberley Molina. Evidence-based evidence: a practical method for Bayesian analysis of forensic evidence. Law, Probability and Risk, 14:135–145, 2015. DOI:10.1093/lpr/mgu019

  • Byron Quan Luna, Luca Garrè, Yongtao Yang. A Bayesian Network approach for risk assessment to a spatially distributed power infrastructure in a GIS environment. Journal of Polish Safety and Reliability Association, Summer Safety and Reliability Seminars, 6(3):127-132, 2015.

  • Arturo Lopez Pineda, Ye Ye, Shyam Visweswaran, Gregory F. Cooper, Michael M. Wagner and Fuchiang (Rich) Tsui. Comparison of machine learning classifiers for influenza detection from emergency department free-text reports. Journal of Biomedical Informatics, 58:60-69, , December 2015.
    DOI: 10.1016/j.jbi.2015.08.019

  • Parot Ratnapinda and Marek J. Druzdzel. Learning discrete Bayesian network parameters from continuous data streams: What is the best strategy? Journal of Applied Logic, 13(4):628-642, Part 2, December 2015.

  • Natasha A. Loghmanpour, Manreet K. Kanwar, Marek J. Druzdzel, Raymond L. Benza, Srinivas Murali and James F. Antaki. A new Bayesian network-based risk stratification model for prediction of short-term and long-term LVAD mortality. ASAIO Journal, 61(3):313-323, May/June 2015.

  • Agnieszka Onisko and Marek J. Druzdzel. Impact of precision of Bayesian networks parameters on accuracy of medical diagnostic systems. Artificial Intelligence in Medicine, 57(3):197-206, March 2013.

  • Natasha A. Loghmanpour, Marek J. Druzdzel and James F. Antaki. Cardiac Health Risk Stratification System (CHRiSS): A Bayesian-based decision support system for Left Ventricular Assist Device (LVAD) therapy. PLoS ONE, 9(11):e111264, November 2014.

  • Kimberley Molina, Vincent DiMaio & Rowena Cave, Gunshot Wounds: A Review of Firearm Type, Range, and Location as Pertaining to Manner of Death. The American Journal of Forensic Medicine and Pathology, 34(4):366-371, December 2013. DOI: 10.1097/PAF.0000000000000065
  • Varkey, D.A., Pitcher, T.J., McAllister, M.K., Sumaila, R.S. Bayesian decision-network modeling of multiple stakeholders for reef ecosystem restoration in the coral triangle. Conservation Biology, 27(3):459–469, 2013.

  • Schmitt, L.H.M., Brugere, C. (2013). Capturing ecosystem services, stakeholders’ preferences and trade-offs in coastal aquaculture decisions: a Bayesian belief network application. PLoS One 8, e75956.

  • Agnieszka Onisko and Marek J. Druzdzel. Impact of precision of Bayesian networks parameters on accuracy of medical diagnostic systems. Artificial Intelligence in Medicine, 57(3):197-206, March 2013.

  • Adam Zagorecki and Marek J. Druzdzel. Knowledge engineering for Bayesian networks: How common are noisy-MAX distributions in practice? IEEE Transactions on Systems, Man, and Cybernetics: Systems, 43(1):186-195, January 2013.

  • Linda C. Santelices, Yajuan Wang, Don Severyn, Marek J. Druzdzel, Robert L. Kormos, James F. Antaki. Development of a hybrid decision support model for optimal ventricular assist device weaning. Annals of Thoracic Surgery, 90:713-720, 2010.

  • R. Marshall Austin, Agnieszka Onisko, Marek J. Druzdzel. The Pittsburgh Cervical Cancer Screening Model: A risk assessment tool. Archives of Pathology and Laboratory Medicine, 134(5):744-750, May 2010.

  • Mark Voortman, Denver H. Dash and Marek J. Druzdzel. Learning causal models that make correct manipulation predictions with time series data. Journal of Machine Learning Research (JMLR) Workshop and Conference Proceedings, Volume 6: Causality: Objectives and Assessment (NIPS 2008), Isabelle Guyon, Dominik Janzing, and Bernhard Scholkopf (eds.), 6:257-266, 2010.

  • Tsai-Ching Lu and Marek J. Druzdzel. Interactive construction of graphical decision models based on causal mechanisms. European Journal of Operations Research (EJOR), 199(3):873-882, December 2009.

  • R. Marshall Austin, Agnieszka Onisko and Marek J. Druzdzel. Bayesian network model analysis as a quality control and risk assessment tool in cervical cancer screening. Journal of Lower Genital Tract Disease, 12(2):160-161, 2008.

  • R. Marshall Austin, Agnieszka Onisko and Marek J. Druzdzel. The Pittsburgh Cervical Cancer Screening Model. Cancer Cytopathology, 114(S5):345, October 2008.

  • Changhe Yuan and Marek J. Druzdzel. Importance sampling algorithms for Bayesian networks: Principles and performance. Mathematical and Computer Modeling, 43(9-10):1189-1207, May 2005.

  • Marek J. Druzdzel. Intelligent decision support systems based on SMILE. Software 2.0, 2(February):12-33, 2005.

  • Haiqin Wang, Denver H. Dash and Marek J. Druzdzel. A method for evaluating elicitation schemes for probabilistic models. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 32(1):38-43, February 2002.

  • Agnieszka Onisko, Marek J. Druzdzel and Hanna Wasyluk. Learning Bayesian network parameters from small data sets: Application of Noisy-OR gates. International Journal of Approximate Reasoning, 27(2):165-182, 2001.

  • Hanna Wasyluk, Agnieszka Onisko and Marek J. Druzdzel. Support of diagnosis of liver disorders based on a causal Bayesian network model. Medical Science Monitor, 7(Suppl. 1):327-332, May 2001.

  • Jian Cheng and Marek J. Druzdzel. AIS-BN: An adaptive importance sampling algorithm for evidential reasoning in large Bayesian networks. Journal of Artificial Intelligence Research (JAIR), 13:155-188, 2000 (Honorable Mention in the 2005 IJCAI-JAIR Best Paper Prize).

Major peer reviewed conferences:

  • Parot Ratnapinda and Marek J. Druzdzel. An empirical evaluation of Bayesian network approximation by arc removal through strength of influence. In Recent Advances in Artificial Intelligence: Proceedings of the Twenty Seventh International Florida Artificial Intelligence Research Society Conference (FLAIRS-2014), William Eberle, Chutima Boonthum-Denecke (eds.), pages 508-511, Menlo Park, CA: AAAI Press, 2014.

  • Parot Ratnapinda and Marek J. Druzdzel. An empirical comparison of Bayesian network parameter learning algorithms for continuous data streams. In Recent Advances in Artificial Intelligence: Proceedings of the Twenty Sixth International Florida Artificial Intelligence Research Society Conference (FLAIRS-2013) Chutima Boonthum-Denecke and G. Michael Youngblood (eds), pages 627-632, Menlo Park, CA: AAAI Press, 2013.

  • I. Nolivos, L. Van Biesen, R.L. Swennen, Modelling an Intensive Banana Cropping System in Ecuador Using aBayesian Network, Proc. XXVIIIth IHC – IS on Engineering Modelling, Monitoring,Mechanization and Automation Tools for Precision Hort. Eds.: W.W. Verstraeten et al. Acta Hort. 919, ISHS, 2011

  • Mark Voortman, Denver H. Dash, Marek J. Druzdzel. Learning why things change: The difference-based causality learner. In Proceedings of the 26th Annual Conference on Uncertainty in Artificial Intelligence (UAI-2010), pages 641-650, AUAI Press, Corvallis, OR, 2010.

  • Mark Voortman and Marek J. Druzdzel. Insensitivity of constraint-based causal discovery algorithms to violations of the assumption of multivariate normality. In Recent Advances in Artificial Intelligence: Proceedings of the Twenty First International Florida Artificial Intelligence Research Society Conference (FLAIRS-2008), David Wilson, H. Chad Lane (eds), pages 690-695, Menlo Park, CA: AAAI Press, 2008.

  • Changhe Yuan and Marek J. Druzdzel. Generalized Evidence Pre-propagated Importance Sampling for Hybrid Bayesian Networks, In Proceedings of the Twenty-Second National Conference on Artificial Intelligence (AAAI-07), pages 1296-1302, Vancouver, British Columbia, Canada, 22-26 July 2007.

  • Changhe Yuan and Marek J. Druzdzel. Improving Importance Sampling by Adaptive Split-Rejection Control in Bayesian Networks, In Proceedings of The 20th Canadian Conference on Artificial Intelligence, pages 332-343, Montreal, Quebec, Canada, 28-30 May 2007.

  • Xiao Xun Sun, Marek J. Druzdzel and Changhe Yuan. Dynamic weighting A* search-based MAP algorithm for Bayesian networks. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (IJCAI-07), pages 2385-2390, 2007.

  • Adam Zagorecki, Mark Voortman and Marek J. Druzdzel. Decomposing local probability distributions in Bayesian networks for improved inference and parameter learning. In Recent Advances in Artificial Intelligence: Proceedings of the Nineteenth International Florida Artificial Intelligence Research Society Conference (FLAIRS-2006), Geoff Sutcliffe & Randy Goebel (eds), pages 860-865, Menlo Park, CA: AAAI Press, 2006.

  • Changhe Yuan and Marek J. Druzdzel. Importance sampling in Bayesian networks: An influence-based approximation strategy for importance functions. In Proceedings of the 21st Annual Conference on Uncertainty in Artificial Intelligence (UAI-05), pages 650-657, AUAI Press, Corvallis, OR, 2005.

  • Changhe Yuan and Marek J. Druzdzel. How heavy should the tails be?. In Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference (FLAIRS-2005), Ingrid Russell & Zdrawko Markov (eds.), Menlo Park, CA: AAA Press, pages 799-804, 2005.

  • Changhe Yuan, Tsai-Ching Lu and Marek J. Druzdzel. Annealed MAP. In Proceedings of the 20th Annual Conference on Uncertainty in Artificial Intelligence (UAI-04), pages 628-635, Morgan Kaufmann Publishers, Inc., San Francisco, CA, 2004.

  • Adam Zagorecki and Marek J. Druzdzel. An empirical study of probability elicitation under Noisy-OR assumption. In Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference (FLAIRS-2004), Valerie Barr & Zdrawko Markov (eds), pages 880-885, Menlo Park, CA: AAA Press, 2004.

  • Denver H. Dash and Marek J. Druzdzel. Robust independence testing for constraint-based learning of causal structure. In Proceedings of the 19th Annual Conference on Uncertainty in Artificial Intelligence (UAI-03), pages 167-174, Morgan Kaufmann Publishers, Inc., San Francisco, CA, 2003.

  • Changhe Yuan and Marek J. Druzdzel. An importance sampling algorithm based on evidence pre-propagation. In Proceedings of the 19th Annual Conference on Uncertainty in Artificial Intelligence (UAI-03), pages 624-631, Morgan Kaufmann Publishers, Inc., San Francisco, CA, 2003.

  • Agnieszka Onisko, Peter Lucas and Marek J. Druzdzel. Comparison of rule-based and Bayesian network approaches in medical diagnostic systems. In Proceedings of the Eighth Annual Conference on Artificial Intelligence in Medicine (AIME-2001), S. Quaglini, P. Barahona, S. Andreassen (eds.) Artificial Intelligence in Medicine, Lecture Notes in Computer Science Subseries, Springer Verlag, pages 281-292, 2001.

  • Jian Cheng and Marek J. Druzdzel. Confidence inference in Bayesian networks. In Proceedings of the Seventeenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-2001), pages 75-82, Morgan Kaufmann Publishers, Inc., San Francisco, CA, 2001.

  • Jian Cheng and Marek J. Druzdzel. Computational investigation of low-discrepancy sequences in simulation algorithms for Bayesian networks. In Proceedings of the Sixteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-2000), pages 72-81, Morgan Kaufmann Publishers, Inc., San Francisco, CA, 2000.

  • Tsai-Ching Lu, Marek J. Druzdzel and Tze-Yun Leong. Causal mechanism-based model construction. In Proceedings of the Sixteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-2000), pages 353-362, Morgan Kaufmann Publishers, Inc., San Francisco, CA, 2000.

  • Haiqin Wang and Marek J. Druzdzel. User interface tools for navigation in conditional probability tables and elicitation of probabilities in Bayesian networks. In Proceedings of the Sixteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-2000), pages 617-625, Morgan Kaufmann Publishers, Inc., San Francisco, CA, 2000.

  • Jian Cheng and Marek J. Druzdzel. Latin hypercube sampling in Bayesian networks. In Proceedings of the Thirteenth International Florida Artificial Intelligence Research Society Conference (FLAIRS-2000), Jim Etheredge & Bill Manaris (eds), pages 287-292, Menlo Park, CA: AAAI Press, 2000.

  • Marek J. Druzdzel. GeNIe: A development environment for graphical decision-analytic models. In Proceedings of the 1999 Annual Symposium of the American Medical Informatics Association (AMIA-1999), page 1206, Washington, D.C., November 6-10, 1999.

  • Marek J. Druzdzel, Agnieszka Onisko, Daniel Schwartz, John N. Dowling and Hanna Wasyluk. Knowledge engineering for very large decision-analytic medical models. In Proceedings of the 1999 Annual Symposium of the American Medical Informatics Association (AMIA-1999), page 1049, Washington, D.C., November 6-10, 1999.

  • Marek J. Druzdzel. SMILE: Structural Modeling, Inference, and Learning Engine and GeNIe: A development environment for graphical decision-theoretic models (Intelligent Systems Demonstration). In Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI-99), pages 902-903, AAAI Press/The MIT Press, Menlo Park, CA, 1999.

Other conferences, workshops, symposia, and book chapters:

  • Lixian Fan, Zimeng Zhang, Jingbo Yin and Xingyuan Wang. The efficiency improvement of port state control based on ship accident Bayesian networks. Journal of Risk and Reliability, 233(1):71-83, 2019. https://doi.org/10.1177%2F1748006X18811199
  • Andreas Philipp and Daniel Goehring. Analytic collision risk calculation for autonomous vehicle navigation. 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada, 20-24 May 2019. https://doi.org/10.1109/ICRA.2019.8793264
  • Przemysław Krata, Roberto Vettor and Carlos Guedes Soares. Bayesian approach to ship speed prediction based on operational data. In: Developments in the Collision and Grounding of Ships and Offshore Structures, CRC Press, Taylor & Francis Group, pages 384-390, 2019.
  • Felipe Sanchez, Davy Monticolo, Eric Bonjour and Jean-Pierre Micaëlli. Use of Bayesian Network Characteristics to Link Project Management Maturity and Risk of Project Overcost. 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), Vol. 1, pages 420-426, 2018. DOI: 10.1109/SITIS.2018.00071
  • Katrina M. Groth, Matthew R. Denman, Michael C. Darling, Thomas B. Jones and George F. Luger. Building and using dynamic risk-informed diagnosis procedures for complex system accidents. Proceedings of the Institution of Mechanical Engineers, Part O Journal of Risk and Reliability 234(3), October 2018. http://dx.doi.org/10.1177/1748006X18803836
  • Eleni I. Georga, Dimitrios Gatsios, Vasilis Tsakanikas, Konstantina D. Kourou, Matthew Liston,
    Marousa Pavlou, Dimitrios Kikidis, Athanasios Bibas, Christos Nikitas, Doris Eva Bamiou, and
    Dimitrios I. Fotiadis. A Dynamic Bayesian Network Approach to behavioral modelling of elderly people during a home-based augmented reality balance. Physiotherapy Programme. Annual International Conference IEEE Eng Med Biol Soc. 5544-5547, July 2020.
  • Sohag Kabir, Ioannis Sorokos, Koorosh Aslansefat, Yiannis Papadopoulos, Youcef Gheraibia, Jan Reich, Merve Saimler and Ran Wei. A Runtime Safety Analysis Concept for Open Adaptive Systems. Springer Nature Switzerland AG 2019, Y. Papadopoulos et al. (Eds.): IMBSA 2019, LNCS 11842, pp. 332–346, 2019.
  • Marcot, B. G., and K. M. Reynolds. EMDS Has a GeNIe With a SMILE. Research Note PNW-RN-581. USDA Forest Service, Pacific Northwest Research Station and Pacific Northwest Region, Portland, Oregon, 2019 https://www.fs.usda.gov/treesearch/pubs/58510.
  • B. Costa, Celeste Jacinto, A.P. Teixeira and Carlos Guedes Soares. Causal analysis of accidents at work in a shipyard complemented with Bayesian nets modelling. Progress in Maritime Technology and Engineering. CRC Press, Taylor & Francis Group, Chapter 48, pages 421-430, 2018.
  • Alberto Tonda, Nadia Boukhelifa, Thomas Chabin, Marc Barnabé and Nathalie Perrot. Interactive Machine Learning for Applications in Food Science. In: Zhou J., Chen F. (eds) Human and Machine Learning. Human–Computer Interaction Series. Springer, Cham, 2018. https://doi.org/10.1007/978-3-319-90403-0_22
  • Birgit Lugrin, Julian Frommel and Elisabeth André. Combining a Data-Driven and a Theory-Based Approach to Generate Culture-Dependent Behaviours for Virtual Characters. In: Faucher C. (eds) Advances in Culturally-Aware Intelligent Systems and in Cross-Cultural Psychological Studies. Intelligent Systems Reference Library, vol 134. Springer, Cham, 2017. https://doi.org/10.1007/978-3-319-67024-9_6
  • Minh-Tuan Nguyen and Thang Cao. A hybrid decision making model for evaluating land combat vehicle system. 22nd International Congress on Modelling and Simulation. Hobart, Tasmania, Australia, December 2017
  • Helen Mayfield, Edoardo Bertone, Oz Sahin and Carl Smith. Structurally aware discretisation for Bayesian networks. 22nd International Congress on Modelling and Simulation, pages 1420-1426, Hobart, Tasmania, Australia, 3-8 December 2017.
  • Yun Huang, Julio Guerra-Hollstein, Jordan Barria-Pineda and Peter Brusilovsky. Learner Modeling for Integration Skills. UMAP’17, pages 85-93, July 9-12, 2017, Bratislava, Slovakia.
  • Jordan Barria-Pineda, Julio Guerra, Yun Huang and Peter Brusilovsky. Concept-Level Knowledge Visualization For Supporting Self-Regulated Learning. IUI ’17 Companion: Proceedings of the 22nd International Conference on Intelligent User Interfaces CompanionMarch 2017 Pages 141–144http://dx.doi.org/10.1145/3030024.3038262
  • Mario A. Cypko, Jan Wojdziak, Matthaeus Stoehr, Bettina Kirchner and Steffen Oeltze-Jafra. Visual Verification of Cancer Staging for Therapy Decision Support. Eurographics Conference on Visualization (EuroVis), J. Heer, T. Ropinski and J. van Wijk (Guest Editors), Volume 36 (2017), Number 3 ,2017.
  • Yasmín Hernández, Marilú Cervantes-Salgado, Miguel Pérez-Ramírez and Manuel Mejía-Lavalle. Data-driven construction of a student model using Bayesian networks in an electrical domain.  In: Pichardo-Lagunas O., Miranda-Jiménez S. (eds) Advances in Soft Computing. MICAI 2016. Lecture Notes in Computer Science, vol 10062. Springer, Cham, 2017. https://doi.org/10.1007/978-3-319-62428-0_39
  • Adam Zagorecki, Marcin Kozniewski and Marek J. Druzdzel. An approximation of surprise index as a measure of confidence. In Self-Confidence in Autonomous Systems, Papers from the AAAI-2015 Fall Symposium, Nisar Ahmed, Mary Cummings, Christopher Miller (eds.), Technical Report FS-15-05, AAAI Press: Palo Alto, CA, pages 39-41.
  • Stanislav Chren and Barbora Buhnova. Local load optimization in smart grids with Bayesian networks. 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016), Budapest, Hungary, October 9-12, 2016. http://dx.doi.org/10.1109/SMC.2016.7844862
  • Vikas Chawla, Hsiang Sing Naik, Adedotun Akintayo, Dermot Hayes, Patrick Schnable, Baskar Ganapathysubramanian and Soumik Sarkar. A Bayesian network approach to county-level corn yield
    prediction using historical data and expert knowledge. KDD ’16 Workshop: Data Science for Food, Energy and Water, San Francisco, CA, USA, August 14,2016.
  • Bastien Grasnick, Harry Freitas Da Cruz, Henriette Dinger, Frank Bier and Christoph Meinel. Early Detection of Acute Kidney Injury with Bayesian Networks. International Symposium on Semantic Mining in Biomedicine, Potsdam, Germany, August 2016.

  • Martijn de Jongh and Marek J. Druzdzel. Evaluation of Rules for Coping with Insufficient Data in Constraint-based Search Algorithms. In Probabilistic Graphical Models, Linda C. van der Gaag and Ad J. Feelders (eds.), Springer Lecture Notes in Computer Science, Vol. 8754, pages 190-205, Springer International Publishing, 2014.

  • Jidapa Kraisangka and Marek J. Druzdzel. Discrete Bayesian Network Interpretation of the Cox’s Proportional Hazard Model. In Probabilistic Graphical Models, Linda C. van der Gaag and Ad J. Feelders (eds.), Springer Lecture Notes in Computer Science, Vol. 8754, pages 238-253, Springer International Publishing, 2014.

  • Krzysztof Nowak and Marek J. Druzdzel. Learning Parameters in Canonical Models using Weighted Least Squares. In Probabilistic Graphical Models, Linda C. van der Gaag and Ad J. Feelders (eds.), Springer Lecture Notes in Computer Science, Vol. 8754, pages 366-381, Springer International Publishing, 2014.

  • Agnieszka Onisko and Marek J. Druzdzel. Impact of Bayesian network model structure on the accuracy of medical diagnostic systems. In Artificial Intelligence and Soft Computing, 13th International Conference, ICAISC 2014, Zakopane, Poland, June 1-5, 2014, Proceedings, Part II, Leszek Rutkowski, Marcin Korytkowski, Rafal Scherer, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Zurada (eds.), Springer Lecture Notes in Computer Science; Lecture Notes in Artificial Intelligence, LNAI 8468, Berlin Heidelberg: Springer-Verlag, pages 167-178, 2014.

  • Anna Lupinska-Dubicka and Marek J. Druzdzel. A Comparison of popular fertility awareness methods to a DBN model of the woman’s monthly cycle. In Proceedings of The Sixth European Workshop on Probabilistic Graphical Models (PGM 2012), Andres Cano, Manuel Gomez & Thomas D. Nielsen (eds.), pages 219-226, 19-21 September 2012, Granada, Spain.

  • Anna Lupinska-Dubicka and Marek J. Druzdzel. Modeling dynamic systems with memory: What is the right time-order?. In Working Notes of the Eight Bayesian Modeling Applications Workshop, Special Theme: Knowledge Engineering, Part of the Annual Conference on Uncertainty in Artificial Intelligence (UAI-2011), pages 75-82, Barcelona, Spain, 14 July 2011.

  • Parot Ratnapinda and Marek J. Druzdzel. Does Query-Based Diagnostics work?. In Working Notes of the Eight Bayesian Modeling Applications Workshop, Special Theme: Knowledge Engineering, Part of the Annual Conference on Uncertainty in Artificial Intelligence (UAI-2011), pages 117-124, Barcelona, Spain, 14 July 2011.

  • Agnieszka Onisko and Marek J. Druzdzel. Impact of quality of Bayesian network parameters on accuracy of medical diagnostic systems.. In Working Notes of the Workshop on Probabilistic Problem Solving in BioMedicine (ProBioMed’11), in conjunction with the Thirteenth Conference on Artificial Intelligence in Medicine (AIME-2011), pages 135-148, Bled, Slovenia, 2 July 2011.

  • Marek J. Druzdzel and Roger R. Flynn. Decision Support Systems. In Encyclopedia of Library and Information Science, Third Edition, Marcia J. Bates and Mary Niles Maack (eds.), Taylor & Francis, Inc., New York, 16 February 2010.

  • John Mark Agosta, Russell Almond, Dennis Buede, Marek J. Druzdzel, Judy Goldsmith and Silja Renooij. Workshop summary: Seventh annual workshop on Bayes applications. In Proceedings of the 26th Annual International Conference on Machine Learning (ICML’09), page 3:1, Montreal, Quebec, Canada, 14-18 June 2009.

  • Mark Voortman, Denver H. Dash, Marek J. Druzdzel, Dean Pomerleau and Gustavo Sudre. Difference-based Causal Models: Bridging the gap between Granger causality and DCMs. In NIPS 2009 Workshop on Connectivity Inference in Neuroimaging (CINI 2009), Whistler, B.C., Canada, December 12th, 2009.

  • Marek J. Druzdzel. Rapid modeling and analysis with QGeNIe. In Proceedings of the International Multiconference on Computer Science and Information Technology (IMCSIT-2009), pages 101-108, Mragowo, Poland, October 12-14, 2009.

  • Parot Ratnapinda and Marek J. Druzdzel. Passive construction of diagnostic decision models: An empirical evaluation. In Proceedings of the International Multiconference on Computer Science and Information Technology (IMCSIT-2009), pages 515-521, Mragowo, Poland, October 12-14, 2009.

  • Marek J. Druzdzel. The role of assumptions in causal discovery. In Proceedings of the 8th Workshop on Uncertainty Processing (WUPES-09), pages 57-68, Liblice, Czech Republic, September 19-23, 2009.

  • Martijn de Jongh and Marek J. Druzdzel. A comparison of structural distance measures for causal Bayesian network models. In Recent Advances in Intelligent Information Systems, Challenging Problems of Science, Computer Science series, Mieczyslaw Klopotek, Adam Przepiorkowski, Slawomir T. Wierzchon, Krzysztof Trojanowski (eds.), pages 443-456, Warsaw: Academic Publishing House EXIT, 2009.

  • F. Javier Diez and Marek J. Druzdzel. Verbal expressions of probability. In Encyclopedia of Medical Decision Making, Kattan, M.W. (Ed.), pages 53-57, Thousand Oaks, CA: Sage Publications, 2009.

  • Agnieszka Onisko, Marek J. Druzdzel and Marshall Austin. Application of Dynamic Bayesian Networks to cervical cancer screening. In Proceedings of Artificial Intelligence Studies, Vol. 6(29), pages 5-14, Siedlce: Publishing House of the University of Podlasie, 2009.

  • Paul P. Maaskant and Marek J. Druzdzel. An ICI Model for opposing influences. In Proceedings of the Fourth European Workshop on Probabilistic Graphical Models (PGM-08), Manfred Jaeger & Thomas D. Nielsen (eds.), pages 185-192, Hirtshals, Denmark, September 17-19, 2008.

  • John M. Agosta and Thomas R. Gardos and Marek J. Druzdzel. Query-based diagnostics. In Proceedings of the Fourth European Workshop on Probabilistic Graphical Models (PGM-08), Manfred Jaeger & Thomas D. Nielsen (eds.), pages 1-8, Hirtshals, Denmark, September 17-19, 2008.

  • Marek J. Druzdzel and Agnieszka Onisko. The Impact of Overconfidence Bias on Practical Accuracy of Bayesian Network Models: An Empirical Study In Working Notes of the 2008 Bayesian Modelling Applications Workshop, Special Theme: How Biased Are Our Numbers?, Part of the Annual Conference on Uncertainty in Artificial Intelligence (UAI-2008), Helsinki, Finland, 9 July 2008.

  • Marek J. Druzdzel and Agnieszka Onisko. Are Bayesian Networks Sensitive to Precision of Their Parameters? In S.T. Wierzchon, M. Klopotek, and M. Michalewicz (eds.), Intelligent Information Systems XVI, Proceedings of the International IIS’08 Conference, pages 35-44, Academic Publishing House EXIT, Warsaw, Poland, June 2008.

  • Anna Lupinska-Dubicka and Marek J. Druzdzel. A dynamic Bayesian network model of womans monthly cycle. In Working notes of the 15th International PTSK (Polskie Towarzystwo Symulacji Komputerowej) Workshop, pages 227-231, Zakopane, Poland, 25-27 September 2008.

  • Katarzyna Kosciuk and Marek J. Druzdzel. Exploring opponent’s weaknesses as an alternative to the Minimax strategy. In Working notes of the 15th International PTSK (Polskie Towarzystwo Symulacji Komputerowej) Workshop, pages 199-210, Zakopane, Poland, 25-27 September 2008.

  • Marek J. Druzdzel and Agnieszka Onisko. Methods for learning diagnostic and risk assessment models from data In 99th ICB Seminar, 7th International Seminar on Statistics and Clinical Practice, page 38, Polish Academy of Sciences, International Center for Biocybernetics, Warsaw, Poland, June 2008.

  • Martinus de Jongh, Marek J. Druzdzel, and Leon Rothkrantz. Implementing and Improving a Method for Non-Invasive Elicitation of Probabilities for Bayesian Networks. In Proceedings of the International Conference on Computer Systems and Technologies (CompSysTech07), pages VI.18.1-VI.18.7, Rousse, Bulgaria, 14-15 June 2007

  • Anna Lupinska-Dubicka and Marek J. Druzdzel. Temporal Aspects of Netflix Data. In Working notes of the 14th International PTSK (Polskie Towarzystwo Symulacji Komputerowej) Workshop. pages 237-244, Krynica Zdroj, Poland, 26-29 September 2007

  • Adam Zagorecki and Marek J. Druzdzel. Probabilistic independence of causal influences. In Proceedings of the Third European Workshop on Probabilistic Graphical Models (PGM-06), pages 325-332, Milan Studeny and Jiri Vomlel (eds.), Prague: Action M Agency, 2006.

  • Changhe Yuan and Marek J. Druzdzel. Hybrid loopy belief propagation. In Proceedings of the Third European Workshop on Probabilistic Graphical Models (PGM-06), pages 317-324, Milan Studeny and Jiri Vomlel (eds.), Prague: Action M Agency, 2006.

  • Xiao Xun Sun, Marek J. Druzdzel and Changhe Yuan. Dynamic weighting A* search-based MAP algorithm for Bayesian networks. In Proceedings of the Third European Workshop on Probabilistic Graphical Models (PGM-06), pages 279-286, Milan Studeny and Jiri Vomlel (eds.), Prague: Action M Agency, 2006.

  • Pieter Kraaijeveld and Marek J. Druzdzel. GeNIeRate: An interactive generator of diagnostic Bayesian network models. In Working Notes of the 16th International Workshop on Principles of Diagnosis (DX-05), pages 175-180, Monterey, CA, USA, June 1-3, 2005.

  • Tsai-Ching Lu and Marek J. Druzdzel. Mechanism-based causal models for adaptive decision support. In Challenges to Decision Support in a Changing World, Papers from the 2005 AAAI Spring Symposium, Marek J. Druzdzel and Tze-Yun Leong (eds.), Technical Report SS-05-02, pages 73-79, Menlo Park, CA: AAAI Press, 2005.

  • Marek J. Druzdzel and Tze-Yun Leong (eds). Challenges to Decision Support in a Changing World, Papers from 2005 AAAI Spring Symposium. AAAI Technical Report SS-05-02, 136 pp., ISBN 1-57735-228-9, March 2005.

  • Changhe Yuan and Marek J. Druzdzel. A comparison on the effectiveness of two heuristics for importance sampling. In Proceedings of the Second European Workshop on Probabilistic Graphical Models (PGM-04), pages 225-232, Leiden, The Netherlands, October 2004.

  • Danier Garcia-Sanchez and Marek J. Druzdzel. An efficient sampling algorithm for influence diagrams. In Proceedings of the Second European Workshop on Probabilistic Graphical Models (PGM-04), pages 97-104, Leiden, The Netherlands, October 2004. Reprinted in Advances in Probabilistic Graphical Models, Studies in Fuzziness and Soft Computing Series, Springer, 213:255-273, 2007. PDF

  • F. Javier Diez, Marek J. Druzdzel and Miguel A. Hernan. Causal diagrams to represent biases in the evaluation of diagnostic procedures. In Proceedings of the 36th Annual Meeting of the Society for Epidemiologic Research (SER-03), Atlanta, GA, 2003.

  • F. Javier Diez and Marek J. Druzdzel. Reasoning Under Uncertainty. In Encyclopedia of Cognitive Science, pages 880-886, Nadel, L. (Ed.), London: Nature Publishing Group, 2003.

  • Agnieszka Onisko and Marek J. Druzdzel. Effect of imprecision in probabilities on the quality of results in Bayesian networks: An empirical study. In Working Notes of the European Conference on Artificial Intelligence in Medicine (AIME-03) Workshop on Qualitative and Model-based Reasoning in Biomedicine, pages 45-49, Protaras, Cyprus, 19 October 2003.

  • Tsai-Ching Lu and Marek J. Druzdzel. Causal models, value of intervention, and search for opportunities. In Proceeding of the First European Workshop on Probabilistic Graphical Models (PGM-02), pages 108-116, Cuenca, Spain, 6-8 November 2002.

  • Hanna Wasyluk, Agnieszka Onisko and Marek J. Druzdzel. Application of a computer-based diagnostic tool to training general practitioners. In Fifth International Seminar on Statistics and Clinical Practice (68-th Seminar of the International Centre of Biocybernetics), Warsaw, Poland, 3-5 June 2002.

  • Agnieszka Onisko, Marek J. Druzdzel and Hanna Wasyluk. An experimental comparison of methods for handling incomplete data in learning parameters of Bayesian networks. In Intelligent Information Systems 2002: Proceedings of the IIS’2002 Symposium, M. Klopotek, S.T. Wierzchon, M. Michalewicz (eds.), pages 351-360, Advances in Soft Computing Series, Physica-Verlag (A Springer-Verlag Company), Heidelberg, 2002.

  • Agnieszka Onisko, Marek J. Druzdzel and Hanna Wasyluk. Learning Bayesian network parameters from data using Noisy-OR gates (in Polish). In Badania operacyjne i systemowe wobec wyzwan XXI wieku, Zdzislaw Bubnicki, Olgierd Hryniewicz, Roman Kulikowski (eds.), Problemy wspolczesnej nauki. Teoria i zastosowania series, pages IV:19-26, Akademicka Oficyna Wydawnicza EXIT, Warszawa, 2002.

  • F. Javier Diez and Marek J. Druzdzel. Fundamentals of canonical models. In Ponencia Congreso: IX Conferencia de la Asociacion Espanola para la Inteligencia Artificial (CAEPIA-TTIA 2001), pages 1125-1134, Gijon, Spain, 2001.

  • Agnieszka Onisko, Leon Bobrowski, Marek J. Druzdzel and Hanna Wasyluk. HEPAR and HEPAR II – computer systems supporting a diagnosis of liver disorders. In Proceedings of the Twelfth Conference on Biocybernetics and Biomedical Engineering, Warsaw, Poland, November 28-30, 2001 (Best Young Investigator Paper award for Ms. Onisko).

  • Marek J. Druzdzel and Roger R. Flynn. Decision Support Systems. In Encyclopedia of Library and Information Science, Vol. 67, Suppl. 30, pages 120-133, Allen Kent (ed.), Marcel Dekker, Inc., New York, 2000.

  • Agnieszka Onisko, Marek J. Druzdzel and Hanna Wasyluk. Learning Bayesian network parameters from small data sets: Application of Noisy-OR gates. In Working Notes of the Workshop on Bayesian and Causal Networks: From Inference to Data Mining, 12th European Conference on Artificial Intelligence (ECAI-2000), Berlin, Germany, 22 August 2000.

  • Marek J. Druzdzel and F. Javier Diez. Criteria for combining knowledge from different sources in probabilistic models. In Working Notes of the workshop on `Fusion of Domain Knowledge with Data for Decision Support,’ Sixteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-2000), pages 23-29, Stanford, CA, 30 June 2000.

  • Agnieszka Onisko, Marek J. Druzdzel and Hanna Wasyluk. Extension of the Hepar II model to multiple-disorder diagnosis. In Intelligent Information Systems, M. Klopotek, M. Michalewicz, S.T. Wierzchon (eds.), pages 303-313,Advances in Soft Computing Series, Physica-Verlag (A Springer-Verlag Company), Heidelberg, 2000.

  • Agnieszka Onisko, Marek J. Druzdzel and Hanna Wasyluk. A Bayesian network model for diagnosis of liver disorders. In Proceedings of the Eleventh Conference on Biocybernetics and Biomedical Engineering, pages 842-846, Warsaw, Poland, December 2-4, 1999.

  • Marek J. Druzdzel and Clark Glymour. Causal inferences from databases: Why universities lose students. In Clark Glymour and Gregory F. Cooper (eds), Computation, Causation, and Discovery, Chapter 19, pages 521-539, AAAI Press, Menlo Park, CA, 1999.

  • Denver H. Dash and Marek J. Druzdzel. Problems related to causal reasoning in equilibrium models. In Proceedings of the Conference on Theoretical Informatics: Methods of Analysis of Incomplete and Distributed Information, pages 24-26, Bialystok, Poland, 26-28 November 1999.

  • Denver H. Dash and Marek J. Druzdzel. A fundamental inconsistency between equilibrium causal discovery and causal reasoning formalisms. In Working Notes of the Workshop on Conditional Independence Structures and Graphical Models, pages 17-18, Fields Institute, Toronto, Canada, 27 September – 1 October 1999.

  • Marek J. Druzdzel. ESP: A mixed initiative decision-theoretic decision modeling system. In Working Notes of the AAAI-99 Workshop on Mixed-initiative Intelligence, pages 99-106, Orlando, Florida, 18 July 1999.

  • Yan Lin and Marek J. Druzdzel. Stochastic sampling and search in belief updating algorithms for very large Bayesian networks. In Working notes of the AAAI-1999 Spring Symposium on Search Techniques for Problem Solving Under Uncertainty and Incomplete Information, pages 77-82, Stanford, CA, March 22-24, 1999.

  • Agnieszka Onisko, Marek J. Druzdzel and Hanna Wasyluk. Graphical probabilistic models in diagnosis of liver disorders. In Working notes of the Third International Seminar on Statistics and Clinical Practice (45th Seminar of the International Centre of Biocybernetics), Warsaw, Poland, June 24-27, 1998.

  • Agnieszka Onisko, Marek J. Druzdzel and Hanna Wasyluk. A probabilistic causal model for diagnosis of liver disorders. In Proceedings of the Seventh Symposium on Intelligent Information Systems (IIS-98), pages 379-387, Malbork, Poland, June 15-19, 1998.

  • Marek J. Druzdzel, Tsai-Ching Lu and Tze-Yun Leong. Interactive construction of decision models based on causal mechanisms. In Working notes of the AAAI 1998 Spring Symposium on Interactive and Mixed-Initiative Decision-Theoretic Systems, pages 38-44, Stanford, CA, March 23-25, 1998.

  • Hans van Leijen and Marek J. Druzdzel. Reversible causal mechanisms in Bayesian networks. In Working notes of the AAAI 1998 Spring Symposium on Prospects for a Commonsense Theory of Causation, pages 24-30, Stanford, CA, March 23-25, 1998.

  • Agnieszka Onisko, Marek J. Druzdzel and Hanna Wasyluk. Application of Bayesian belief networks to diagnosis of liver disorders. In Proceedings of the Third Conference on Neural Networks and Their Applications, pages 730-736, Kule, Poland, October 14-18, 1997.

  • Marek J. Druzdzel. An incompatibility between preferential ordering and the decision-theoretic notion of utility. In Working notes of the AAAI 1997 Spring Symposium on Qualitative Preferences in Deliberation and Practical Reasoning, pages 35-40, Stanford, CA, March 23-25, 1997.

  • Marek J. Druzdzel. Technology use in computer programming courses. In Second Annual University of Pittsburgh Teaching Excellence Conference: Technology in Teaching, Pittsburgh, PA, March 29, 1996.

  • Marek J. Druzdzel and Clark Glymour. Having the right tool: Causal graphs in teaching research design. In What Works in University Teaching: University of Pittsburgh Teaching Excellence Conference, Pittsburgh, PA, March 31 – April 1, 1995.

Other

  • Christopher Ashley Ford. Decision-Support Tools for National Policymakers: Fool’s Gold or Treasure Trove? MITRE Solving Problems for a Safer World Technical Report Series, 11 January 2022, Available at https://www.mitre.org/publications/technical-papers/decision-support-tools-for-national-policymakers-fools-gold-or-treasure-trove
  • Rui Zhang. Modelling Fish Occurrence in the Dutch Wadden Sea by using Bayesian Network Structure Learning Algorithm. M.Sc. thesis, Leiden University, Department of Computer Science, 2019.
  • Aldo Benini. Bayesian belief networks – Their use in humanitarian scenarios An invitation to explorers. Monitoring and Evaluation NEWS, 24 September 2018. https://mande.co.uk/2018/media-3/unpublished-paper/bayesian-belief-networks-their-use-in-humanitarian-scenarios-an-invitation-to-explorers/
  • Felipe Sanchez Garzon. Supporting the transformation of a company’s project management by elaborating an invariant-based project management maturity model and a causal predictive model between maturity criteria and project performance. Business administration. Université de Lorraine, 2019.
  • Yun Huang. Learner Modeling for Integration Skills in Programming. Doctoral Dissertation, University of Pittsburgh, 2018. http://d-scholarship.pitt.edu/35176/