Bayesian Net References Version 4 13 July 2008



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  • Dahll, G. and B. A. Gran (2000). The use of Bayesian belief nets in safety assessment of software based systems. OECD Halden Reactor Project, PO Box 173, N-1751 Halden, Norway

  • Dai, C. and J. Liu (2005). "Inducing Pairwise Gene Interactions from Time-Series Data by EDA Based Bayesian Network." Conf Proc IEEE Eng Med Biol Soc 7: 7746-9

  • Darwiche, A. Constant-Space Reasoning in Dynamic Bayesian Networks http://citeseer.ist.psu.edu/489080.html.

  • Darwiche, A. (2003). "Differential Approach to Inference in Bayesian Networks." Journal of the ACM 50(3): 280-305

  • Das, B. (2004). "Generating Conditional Probabilities for Bayesian Networks: Easing the Knowledge Acquisition Problem. http://www.arxiv.org [On-line]. Available: http://www.citebase.org/cgi-bin/citations?id=oai:arXiv.org:cs/0411034."

  • Dawid, A. P. (2003). An object-oriented Bayesian network for estimating mutation rates. Ninth International Workshop on Artificial Intelligence and Statistics, ISBN 0-9727358-0-1. C. M. Bishop and B. J. Frey. Key West, Florida http://tinyurl.com/39bmh.

  • Dawid, A. P., J. Mortera, et al. (2006). Representing and solving complex DNA identification cases using Bayesian networks. Progress in Forensic Genetics 11 (Proceedings of the 21st International ISFG Congress). A. Amorim, F. Corte-Real and N. Morling. Ponta Delgada, The Azores, Portugal International Congress Series, Elsevier Science, Amsterdam. 1288: 484-91

  • Dawid, A. P., J. Mortera, et al. (2007). "Object-oriented Bayesian networks for complex forensic DNA profiling problems." Forensic Science International 169: 195-205

  • de Campos, L. M. (2006). "A Scoring Function for Learning Bayesian Networks based on Mutual Information and Conditional Independence Tests." J. Mach. Learn. Res. 7: 2149-2187

  • de Campos, L. M. (2006). "A Scoring Function for Learning Bayesian Networks based on Mutual Information and Conditional Independence Tests." J. Mach. Learn. Res. 7: 2149-2187

  • de Melo, A. C. V. and A. J. Sanchez (2008). "Software maintenance project delays prediction using Bayesian Networks." Expert Syst. Appl. 34(2): 908-919 http://dx.doi.org/10.1016/j.eswa.2006.10.040.

  • De Santa Olalla, F. J. M., Dominguez A, et al. (2005). "Integrated Water Resources Management of the Hydrogeological Unit "Eastern Mancha" Using Bayesian Belief Networks." Agricultural Water Management 77(1-3): 21-36

  • de Santana, A. L., C. R. Frances, et al. (2007). "Strategies for improving the modeling and interpretability of Bayesian networks." Data Knowl. Eng. 63(1): 91-107

  • Deforche, K., R. Camacho, et al. (2007). "Bayesian network analysis of resistance pathways against HIV-1 protease inhibitors." Infect Genet Evol 7(3): 382-90

  • Delic, K. A., F. Mazzanti, et al. (1997). Formalising a software safety case via belief networks. Proc DCCA-6, 6th IFIP International Working Conf on Dependable Computing for critical Appliations, Garmisch-Partenkirchen, Germany, March

  • Dembo, R., D. Farrow, et al. (1979). "Testing a causal model of environmental influences on the early drug involvement of inner city junior high school youths." Am J Drug Alcohol Abuse 6(3): 313-36

  • Deng, X., H. Geng, et al. (2006). "Joint learning of gene functions--a Bayesian network model approach." J Bioinform Comput Biol 4(2): 217-39

  • Dexheimer, J. W., L. E. Brown, et al. (2007). "Comparing decision support methodologies for identifying asthma exacerbations." Stud Health Technol Inform 129: 880-4

  • Dey, S. and J. A. Stori (2005). "A Bayesian network approach to root cause diagnosis of process variations." International Journal of Machine Tools and Manufacture 45(1): 75-91

  • Diamond, L., V. Mishka, et al. (1995). "Are normative expert systems appropriate for diagnostic pathology?" J Am Med Inform Assoc 2(2): 85-93

  • Díez, F. J. (1993). Parameter adjustment in Bayes networks: the gener-alized noisy or-gate. Ninth Conference on Uncertainty in Artificial Intelligence. D. Heckerman and A. Mamdani. Washington D.C: 99-105

  • Diez, F. J., J. Mira, et al. (1997). "DIAVAL, a Bayesian expert system for echocardiography." Artif Intell Med 10(1): 59-73

  • Dockstader, S. L. and A. M. Tekalp (2001). "Multiple camera tracking of interacting and occluded human motion." Proceedings of the IEEE 89(10): 1441-1455

  • Donaghy, R. and A. H. Marshall (2005). Modelling the Health of ARMD Patients Eyes Using a Dynamic Bayesian Network. Conference on Applied Statistics. Ireland, Enniskillen: 98-99

  • Downe-Wamboldt, B. L. and P. M. Melanson (1998). "A causal model of coping and well-being in elderly people with arthritis." J Adv Nurs 27(6): 1109-16

  • Dray, P., G. J. Bearfield, et al. (2007). Constructing Scalable and Parameterised System Wide Risk Models. 25th International System Safety Conference. Baltimore, USA, System Safety Society

  • Druzdzel, M. J. and H. van Leijen (2001). "Causal reversibility in Bayesian networks." Journal of Experimental and Theoretical Artificial Intelligence 13(1): 45-62

  • Druzdzel, M. K. and L. C. van der Gaag (2000). "Building Probabilistic Networks: Where Do the Numbers Come From?" IEEE Transactions on Knowledge and Data Engineering 12(4): 481-486

  • Druzdzel, M. K. and L. C. vanderGaag (1995). Elicitation of probabilities for belief networks: combining qualitative and quantitiative information. Proc 11th Ann Conf on Uncertainty in Artifical Intelligence (UAI-95), 141-148, Montreal, Quebec, Canada, August

  • Eaton, D. and K. Murphy (2007). "Exact Bayesian structure learning from uncertain interventions." AI & Statistics

  • Edwards, D. (2000). Introduction to Graphical Modelling, Springer-Verlag.

  • Edwards, W. (1991). "Influence Diagrams, Bayesian Imperialism, and the Collins case: an appeal to reason." Cardozo Law Review 13: 1025-1079

  • Eleye-Datubo, A., A. Wall, et al. (2006). "Enabling a powerful marine and offshore decision-support solution through Bayesian network technique." Risk Anal 26(3): 695-721

  • Elish, M. O., D. C. Rine, et al. (2002). "Evaluating collaborative software in supporting organizational learning with Bayesian Networks." SAC '02: Proceedings of the 2002 ACM symposium on Applied computing: 992-996 http://doi.acm.org/10.1145/508791.508984.

  • Elloy, D. F., W. Terpening, et al. (2001). "A causal model of burnout among self-managed work team members." J Psychol 135(3): 321-34

  • Embrey, D. E. (1992). Incorporating management and organisational factors into probabilistic safety assessment. Reliability Engineering and System Safety, 38, 199-208

  • Erb, R. J. (1995). "The backpropagation neural network--a Bayesian classifier. Introduction and applicability to pharmacokinetics." Clin Pharmacokinet 29(2): 69-79

  • Evett, I. W., P. D. Gill, et al. (2002). "Interpreting small quantities of DNA: the hierarchy of propositions and the use of Bayesian networks." Journal of Forensic Sciences 47(3): 520-530

  • Ezawa, K., M. Singh, et al. (1996). Learning Goal-Oriented Bayesian Networks for Telecommunications Risk Management. 13th International Conference on Machine Learning. Bari, Italy: 139-147

  • Faigman, D. L. and A. J. Baglioni (1988). "Bayes' theorem in the trial process." Law and Human Behavior 12(1): 1-17 http://dx.doi.org/10.1007/BF01064271.

  • Falzon, L. (2005). "Using Bayesian network analysis to support centre of gravity analysis in military planning." European Journal of Operational Research 170 (2): 629-643

  • Fan, C.-F. and Y.-C. Yu (2004). "BBN-based software project risk management." J Systems Software 73 (2): 193-203

  • Federal Drugs Agency (2006 ). Guidance for the Use of Bayesian Statistics in Medical Device Clinical Trials - Draft Guidance for Industry and FDA Staff, U.S. Department of Health and Human Services, Food and Drug Administration http://www.fda.gov/cdrh/osb/guidance/1601.html

  • Feelders, A. J. and L. C. v. d. Gaag (2006). "Learning Bayesian network parameters under order constraints." International Journal of Approximate Reasoning (IJAR) 42: 37-53

  • Feelders, A. J. and J. Ivanovs (2006). Discriminative Scoring of Bayesian Network Classifiers: a Comparative Study. Third European workshop on probabilistic graphical models (PGM'06) M. Studen'y and J. Vomlel: 75-82

  • Fenton, N. E. (2003). SCULLY: Scaling up Bayesian Nets for Software Risk Assessment. EPSRC Project GR/N00258 Final Report. Queen Mary University of London, EPSRC http://www.dcs.qmul.ac.uk/research/radar/Projects/scully/SCULLY%20Project%20Final%20Report.pdf.

  • Fenton, N. E., B. Littlewood, et al. (1998). "Assessing dependability of safety critical systems using diverse evidence." IEE Proceedings Software 145(1): 35-39

  • Fenton, N. E., D. W. R. Marsh, et al. (1999). SERENE (SafEty and Risk Evaluation using bayesian Nets): Method Manual https://www.dcs.qmul.ac.uk/~norman/papers/serene.pdf.

  • Fenton, N. E., W. Marsh, et al. (2004). Making Resource Decisions for Software Projects. 26th International Conference on Software Engineering (ICSE2004) Edinburgh, United Kingdom, IEEE Computer Society: 397-406

  • Fenton, N. E. and M. Neil (1999). "A critique of software defect prediction models." Software Engineering, IEEE Transactions on 25(5): 675-689

  • Fenton, N. E. and M. Neil (1999). "Software metrics: successes, failures and new directions." Journal of Systems and Software 47(2-3): 149-157

  • Fenton, N. E. and M. Neil (2000). "Bayesian belief nets: a causal model for predicting defect rates and resource requirements." Software Testing and Quality Engineering 2(1): 48-53

  • Fenton, N. E. and M. Neil (2000). "The Jury Fallacy and the use of Bayesian nets to simplify probabilistic legal arguments." Mathematics Today (Bulletin of the IMA) 36(6): 180-187

  • Fenton, N. E. and M. Neil (2001). "Making Decisions: Using Bayesian Nets and MCDA." Knowledge-Based Systems 14: 307-325

  • Fenton, N. E. and M. Neil (2007). Managing Risk in the Modern World: Bayesian Networks and the Applications, London Mathematical Society, Knowledge Transfer Report. 1 http://www.lms.ac.uk/activities/comp_sci_com/KTR/apps_bayesian_networks.pdf.

  • Fenton, N. E., M. Neil, et al. (2007). "Using Ranked nodes to model qualitative judgements in Bayesian Networks." IEEE Transactions on Knowledge and Data Engineering 19(10): 1420-1432

  • Fenton, N. E., M. Neil, et al. (2002). "Software Measurement: Uncertainty and Causal Modelling." IEEE Software 10(4): 116-122

  • Fenton, N. E., M. Neil, et al. (2008). "Using Bayesian Networks to Predict Software Defects and Reliability." Proceedings of the Institution of Mechanical Engineers, Part O, Journal of Risk and Reliability to appear

  • Fenton, N. E., M. Neil, et al. (2007). "Predicting software defects in varying development lifecycles using Bayesian nets." Information & Software Technology 49: 32-43

  • Fenton, N. E., L. Radlinski, et al. (2006). Improved Bayesian Networks for Software Project Risk Assessment Using Dynamic Discretisation. Software Engineering Techniques: Design for Quality (Prceedings of Software Engineering Techniques 2006, Warsaw, Poland, 17-20 Oct 2006). K. Sacha, Springer, Boston. 227: 139-148.

  • Ferat, S., Y. M Cetin, et al. (2007). "Fault diagnosis for airplane engines using Bayesian networks and distributed particle swarm optimization." Parallel Comput. 33(2): 124-143

  • Fernández, A. and A. Salmerón (2008). "BayesChess: A computer chess program based on Bayesian networks." Pattern Recognition Letters 29 (8): 1154-1159

  • Fine, S. and A. Ziv (2003). Coverage Directed Test Generation for Functional Verification using Bayesian Networks. 40th Design Automation Conference: 286-291

  • Fine, S. and A. Ziv (2004). On the application of Bayesian Networks for simulation-based verification. 2nd Bayesian Modelling Applications Workshops of Uncertainty in AI, Banff, Canada

  • Fischer, E. A., J. Y. Lo, et al. (2004). "Bayesian networks of BI-RADStrade mark descriptors for breast lesion classification." Conf Proc IEEE Eng Med Biol Soc 4: 3031-4

  • Foreman, L. A., C. Champod, et al. (2003). "Interpreting DNA Evidence: A Review." Internat. Statist. Rev. 71(3): 473-495

  • Forshed, J., F. O. Andersson, et al. (2002). "NMR and Bayesian regularized neural network regression for impurity determination of 4-aminophenol." J Pharm Biomed Anal 29(3): 495-505

  • Freedman, D. A. (2004). "Graphical models for causation, and the identification problem." Eval Rev 28(4): 267-93

  • Friedman, N. and M. Goldszmidt (1998). Learning Bayesian Network from Data. SRI International. http://www.erg.sri.com/people/moises/tutorial/index.htm

  • Fu, L. D. and I. Tsamardinos (2005). "A comparison of Bayesian network learning algorithms from continuous data." AMIA Annu Symp Proc: 960

  • Fugelsang, J. A. and V. A. Thompson (2003). "A dual-process model of belief and evidence interactions in causal reasoning." Mem Cognit 31(5): 800-15

  • Fung, R. and B. Del Favero (1995). "Applying Bayesian Networks to Information Retrieval." Communications of the ACM 38(3): 42-48

  • Galliers, J., A. Sutcliffe, et al. (1999). A causal model of human error for safety-critical user interface design. City University, CHCID

  • Garbolino, P. and F. Taroni (2002). "Evaluation of scientific evidence using Bayesian networks." Forensic Sci. Int. 125 149-155

  • Garc, P., A. Amandi, et al. (2007). "Evaluating Bayesian networks' precision for detecting students' learning styles." Comput. Educ. 49(3): 794-808

  • Geiger, D. and D. Heckerman (1996). "Knowledge representation and inference in similarity networks and Bayesian multinets." Artifical Intelligence J 82((1-2)): 45-74

  • Gevaert, O., F. De Smet, et al. (2006). "Predicting the outcome of pregnancies of unknown location: Bayesian networks with expert prior information compared to logistic regression." Human Reproduction 21(7): 1824-1831

  • Gevaert, O., F. De Smet, et al. (2006). "Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks." Bioinformatics 22(14): e184-90

  • Ghabayen, S. M. S., M. McKee, et al. (2006). "Ionic and isotopic ratios for identification of salinity sources and missing data in the Gaza aquifer." Journal of Hydrology 318(1-4): 360-373

  • Gilthorpe, M., I. Maddick, et al. (2000). "Introduction to Bayesian modelling in dental research." Community Dent Health 17(4): 218-21

  • Gjerdingen, D. K., D. G. Froberg, et al. (1990). "A causal model describing the relationship of women's postpartum health to social support, length of leave, and complications of childbirth." Women Health 16(2): 71-87

  • Glymour, C. (2001). The Mind's Arrows: Bayes Nets and Graphical Causal Models in Psychology. Cambridge, MA: , The MIT Press.

  • Golightly, A. and D. J. Wilkinson (2006). "Bayesian sequential inference for stochastic kinetic biochemical network models." J Comput Biol 13(3): 838-51

  • Gong, S., J. Ng, et al. (2002). "On the semantics of visual behaviour, structured events and trajectories of human action." Image and Vision Computing 20(12): 873-888

  • Goubanova, O. and S. King (2008). "Bayesian networks for phone duration prediction." Speech Commun. 50(4): 301-311

  • Gran, B. A. (2002). "Assessment of programmable systems using Bayesian belief nets

  • " Safety Science 40(9): 797-812

  • Gras, J.-J. (2004). "End-to-End Defect Modeling." IEEE Software 21(5): 98-100 http://doi.ieeecomputersociety.org/10.1109/MS.2004.1331312.

  • Green, N. (2005). "A Bayesian network coding scheme for annotating biomedical information presented to genetic counseling clients." J Biomed Inform 38(2): 130-44

  • Greenland, S. (1990). "Randomization, statistics, and causal inference." Epidemiology 1(6): 421-9

  • Greenland, S. and B. Brumback (2002). "An overview of relations among causal modelling methods." Int J Epidemiol 31(5): 1030-7

  • Greenland, S. and H. Morgenstern (2001). "Confounding in health research." Annu Rev Public Health 22: 189-212

  • Greenland, S., J. Pearl, et al. (1999). "Causal diagrams for epidemiologic research." Epidemiology 10(1): 37-48

  • Gregoriades, A. and A. Sutcliffe (2005). "Scenario-based assessment of nonfunctional requirements." Software Engineering, IEEE Transactions on 31(5): 392-409

  • Griffin, H. C., C. L. Fitch, et al. (2004). "The causal pathway model and cerebral palsy." Neonatal Netw 23(6): 43-7

  • Haddawy, P., J. Jacobson, et al. (1997). "BANTER: a Bayesian network tutoring shell." Artif Intell Med 10(2): 177-200

  • Haddawy, P., C. E. Kahn, Jr., et al. (1994). "A Bayesian network model for radiological diagnosis and procedure selection: work-up of suspected gallbladder disease." Med Phys 21(7): 1185-92

  • Hajmeer, M. N. and I. A. Basheer (2003). "A hybrid Bayesian-neural network approach for probabilistic modeling of bacterial growth/no-growth interface." Int J Food Microbiol 82(3): 233-43

  • Hall, J. A., M. A. Milburn, et al. (1993). "A causal model of health status and satisfaction with medical care." Med Care 31(1): 84-94

  • Halliwell, J., J. Keppens, et al. (2003). "Linguistic Bayesian Networks for reasoning with subjective probabilities in forensic statistics." ICAIL '03: Proceedings of the 9th international conference on Artificial intelligence and law: 42-50

  • Hamilton, P. W., N. Anderson, et al. (1994). "Expert system support using Bayesian belief networks in the diagnosis of fine needle aspiration biopsy specimens of the breast." J Clin Pathol 47(4): 329-36

  • Hansen, C. P. (1989). "A causal model of the relationship among accidents, biodata, personality, and cognitive factors." J Appl Psychol 74(1): 81-90

  • Hatsch, D., C. Keyser, et al. (2007). "Resolving paternity relationships using X-chromosome STRs and Bayesian networks." J Forensic Sci 52(4): 895-7

  • Hearty, P., N. Fenton, et al. (2007). "Predicting Project Velocity in XP using a Learning Dynamic Bayesian Network Model." IEEE Trans Software Eng (submitted)

  • Hearty, P., N. Fenton, et al. (2005). Automated population of causal models for improved software risk assessment. ASE '05: Proceedings of the 20th IEEE/ACM international Conference on Automated software engineering, Long Beach, CA, USA, ACM Press.

  • Heckerman and EricHorvitz (1998). Inferring Informational Goals from Free-Text Queries: A Bayesian Approach. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, Madison, WI, pages 230-237. Morgan Kaufmann: San Francisco, July

  • Heckerman, D. and J. S. Breese (1995). Causal independence for probability assessment and inference using Bayesian networks. Microsoft Technical Report MSR-TR-94-08, to appear in IEEE Systems Man and Cybernetics

  • Heckerman, D., D. Geiger, et al. (1995). Learning Bayesian networks: trhe combination of knowledge and statistical data. Machine Learning, 20, 197-243

  • Heckerman, D., A. Mamdani, et al. (1995). Real-world applications of Bayesian networks. Comm ACM, 38(3), 25-26

  • Hejlesen, O., S. Andreassen, et al. (1997). "DIAS--the diabetes advisory system: an outline of the system and the evaluation results obtained so far." Comput Methods Programs Biomed 54(1-2): 49-58

  • Hejlesen, O. K., K. G. Olesen, et al. (2005). "Decision support for diagnosis of lyme disease." Stud Health Technol Inform 116: 205-10

  • Helman, P., R. Veroff, et al. (2004). "A Bayesian network classification methodology for gene expression data." J Comput Biol 11(4): 581-615

  • Helsper, E. M., L. C. v. d. Gaag, et al. (2005). Bringing order into Bayesian-network construction. Third International Conference on Knowledge Capture, New York: ACM Press: 121-128

  • Henderson, J. S. and R. W. Burn (2004). "Uptake pathways: the potential of Bayesian belief networks to assist the management, monitoring and evaluation of development-orientated research." Agricultural Systems 79(1): 3-15

  • Henriksen, H. J., P. Rasmussen, et al. (2007). "Public participation modelling using Bayesian networks in management of groundwater contamination." Environmental Modelling & Software 22(8): 1101-1113

  • Henrion, M. (1989). Some Practical Issues in Constructing Belief Networks. Uncertainty in Artificial Intelligence 3. L. Kanal, T. Levitt and J. Lemmer, North Holland: Elsevier Science: 161-173

  • Hepler, A. B., A. P. Dawid, et al. (2007). "Object-oriented graphical representations of complex patterns of evidence." Law, Probability & Risk 6(1-4): 275-293

  • Hepler, A. B. and B. S. Weir (2004). Using Bayesian Networks for Paternity Calculations: Adding an Evolutionary Perspective. JSM (Joint Statistical Meetings) 2004. Toronto, Canada

  • Hernan, M. A. (2004). "A definition of causal effect for epidemiological research." J Epidemiol Community Health 58(4): 265-71

  • Hernan, M. A. and J. M. Robins (2006). "Instruments for causal inference: an epidemiologist's dream?" Epidemiology 17(4): 360-72

  • Herskovits, E. H. and G. F. Cooper (1991). "Algorithms for Bayesian belief-network precomputation." Methods Inf Med 30(2): 81-9

  • Hibou, M. and J.-M. Labat (2006). How to orientate arcs in a Bayesian network based student model. 6th IEEE International Conference on Advance Learning Technologies: 560-562

  • Hoeffer, B. (1987). "A causal model of loneliness among older single women." Arch Psychiatr Nurs 1(5): 366-73

  • Hofler, M. (2005). "The Bradford Hill considerations on causality: a counterfactual perspective." Emerg Themes Epidemiol 2: 11

  • Hofler, M. (2005). "Causal inference based on counterfactuals." BMC Med Res Methodol 5: 28

  • Hofler, M. (2006). "Getting causal considerations back on the right track." Emerg Themes Epidemiol 3: 8

  • Hogan, J. W. and D. O. Scharfstein (2006). "Estimating causal effects from multiple cycle data in studies of in vitro fertilization." Stat Methods Med Res 15(2): 195-209

  • Holst, A. and A. Lansner (1993). "A flexible and fault tolerant query-reply system based on a Bayesian neural network." Int J Neural Syst 4(3): 257-67

  • Hoot, N. and D. Aronsky (2005). "Using Bayesian networks to predict survival of liver transplant patients." AMIA Annu Symp Proc: 345-9

  • Horn, J., T. Birkhölzer, et al. (2001). Knowledge Acquisition and Automated Generation of Bayesian Networks for a Medical Dialogue and Advisory System. Lecture Notes In Computer Science; Vol. 2101 Proceedings of the 8th Conference on AI in Medicine in Europe: Artificial Intelligence Medicine Pages: 199 - 202.

  • Horvitz, E. (1998). Lumiere Project: Bayesian Reasoning for Automated Assistance, Decision Theory & Adaptive Systems Group, Microsoft Research. Microsoft Corp. Redmond, WA http://research.microsoft.com/research/dtg/horvitz/lum.htm.

  • Horvitz, E. and M. Barry (1995). Display of information for time-critical decision making. 11th Conference on Uncertainty in Artificial Intelligence. Montreal

  • Horvitz, E., J. Breese, et al. (1998). The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users. Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence


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