Bayesian Net References Version 4 13 July 2008



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Bayesian Net References
Version 4
13 July 2008

This document contains a list of references to publications and reports about Bayesian Net technology, and especially Bayesian Net applications. The report will be regularly updated and we welcome suggestions for new references to be added. Please send new references for inclusion to norman@agenarisk.com




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  1. Abdel-Hamid, T. K. (1996). The slippery path to productivity improvement. IEEE Software, 13(4), 43-52

  2. Abderrahim, D., L. Bernard, et al. (2006). TIDES - Using Bayesian Networks for Student Modeling. Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies, IEEE Computer Society: 1002 - 1007

  3. Abramson, B. (1994). "The design of belief network-based systems for price forecasting." Computers & Electrical Engineering 20(2): 163-180

  4. Abramson, B., J. Brown, et al. (1996). "HAILFINDER: A Bayesian system for predicting extreme weather." International Journal of Forecasting 7: 57-78

  5. Ackerman, F. and C. Eden (2005). "Using Causal Mapping with Group Support Systems to Elicit an Understanding of Failure in Complex Projects: Some Implications for Organizational Research." Group Decision and Negotiation 14: 355–376

  6. Ackermann, F., C. Eden, et al. (1997). "Modeling for Litigation: Mixing Qualitative and Quantitative Approaches." Interfaces 27: 48-65

  7. Aires, F., C. Prigent, et al. (2004). "Neural network uncertainty assessment using Bayesian statistics: a remote sensing application." Neural Comput 16(11): 2415-58

  8. Aitken, C. (1996). "Lies, damned lies and expert witnesses." Mathematics Today (Bulletin of the IMA) 32(5/6): 76-80

  9. Aitken, C., F. Taroni, et al. (2003). "A graphical model for the evaluation of cross-transfer evidence in DNA profiles." Theoretical Population Biology 63: 179-190

  10. Aitken, C. G. G. (2004 ). Statistical interpretation of evidence: Bayesian analysis, Joseph Bell Centre for Forensic Statistics & Legal Reasoning http://www.cfslr.ed.ac.uk/publications/a001.pdf.

  11. Aitken, C. G. G., T. Connolly, et al. (1995). Bayesian belief networks with an application in specific case analysis. Computational Learning and Probabilistic Reasoning. A. Gammerman, John Wiley and Sons Ltd.

  12. Aitken, C. G. G., T. Connolly, et al. (1996). "Statistical modelling in specific case analysis." Science & Justice: 36(4): 245-255

  13. Aktaşa, E., F. Ülengin, et al. (2007). "A decision support system to improve the efficiency of resource allocation in healthcare management." Socio-Economic Planning Sciences 41(2): 130-146

  14. Aliferis, C. F. and G. F. Cooper (1996). An Evaluation of an Algorithm for Inductive Learning of Bayesian Belief Networks Using Simulated Data Sets. Section of Medical Informatics & Intelligent Systems Program,University of Pittsburg

  15. Aliferis, C. F. and G. F. Cooper (1996). A Structurally and Temporally Extended Bayesian Belief Network Model: Definitions, Properties, and Modelling Techniques. cons@smi.med.pitt.edu

  16. Alterovitz, G., M. Xiang, et al. (2007). "GO PaD: the Gene Ontology Partition Database." Nucleic Acids Res 35(Database issue): 322-7

  17. Alvarez, S. M., B. A. Poelstra, et al. (2006). "Evaluation of a Bayesian decision network for diagnosing pyloric stenosis." J Pediatr Surg 41(1): 155-61; discussion 155-61

  18. Amasaki, S., O. Mizuno, et al. (2003). A Bayesian Belief Network for Predicting Residual Faults in Software Products. Proceedings of 14th International Symposium on Software Reliability Engineering (ISSRE2003), November, pp. 215-22

  19. An, X., D. Jutla, et al. (2006). Privacy intrusion detection using dynamic Bayesian networks. Proceedings of the 8th international conference on Electronic commerce: The new e-commerce: innovations for conquering current barriers, obstacles and limitations to conducting successful business on the internet. Fredericton, New Brunswick, Canada, ACM: 208 - 215

  20. Anderson, S. K., K. G. Olesen, et al. (2000). HUGIN - a shell for building Bayesian belief universes for expert systems. 11th Intl Joint Conf Artifical Intelligence. Detroit: 1080-1085

  21. Andreassen, M. Woldbye, et al. (1987). MUNIN: a causal probabilistic network for interpretation of electromyographic findings. 10th International Joint Conference on Artificial Intelligence. Milan, Italy: 366-372

  22. Andreassen, S., F. Jensen, et al. (1991). "Medical expert systems based on causal probabilistic networks." Int J Biomed Comput 28(1-2): 1-30

  23. Andreassen, S., C. Riekehr, et al. (1999). "Using probabilistic and decision-theoretic methods in treatment and prognosis modeling." Artif Intell Med 15(2): 121-34

  24. Antal, P., G. Fannes, et al. (2003). "Bayesian applications of belief networks and multilayer perceptrons for ovarian tumor classification with rejection." Artif Intell Med 29(1-2): 39-60

  25. Antal, P., G. Fannes, et al. (2004). "Using literature and data to learn Bayesian networks as clinical models of ovarian tumors." Artif Intell Med 30(3): 257-81

  26. Arens, D. A. (1982). "Widowhood and well-being: an examination of sex differences within a causal model." Int J Aging Hum Dev 15(1): 27-40

  27. Aronsky, D., M. Fiszman, et al. (2001). "Combining decision support methodologies to diagnose pneumonia." Proc AMIA Symp: 12-6

  28. Aronsky, D. and P. J. Haug (1998). "Diagnosing community-acquired pneumonia with a Bayesian network." Proc AMIA Symp: 632-6

  29. Aronsky, D. and P. J. Haug (2000). "Automatic identification of patients eligible for a pneumonia guideline." Proc AMIA Symp: 12-6

  30. Astakhov, V. and A. Cherkasov (2005). "Prediction of HLA-A2 binding peptides using Bayesian network." Bioinformation 1(2): 58-63

  31. Athanasiou, M. and J. Y. Clark (2007). A Bayesian Network Model for the Diagnosis of the Caring Procedure for Wheelchair Users with Spinal Injury. Twentieth IEEE International Symposium on Computer-Based Medical Systems (CBMS'07) 433-438

  32. Bacon, P. J., J. D. Cain, et al. (2002). "Belief network models of land manager decisions and land use change." Journal of Environmental Management 65(1): 1-23

  33. Bahrami, H. (2006). "Causal models in primary open angle glaucoma." Ophthalmic Epidemiol 13(4): 291-8

  34. Bai, C.-G. (2005). "Bayesian network based software reliability prediction with an operational profile." J. Syst. Softw. 77(2): 103-112

  35. Bai, C. G., Q. P. Hu, et al. (2005). "Software failure prediction based on a Markov Bayesian network model." J. Syst. Softw. 74(3): 275-282

  36. Baker, M. (2000). Diagnostic system utilizing a Bayesian network model having link weights updated experimentally. Patent number: 6076083

  37. Bang, J. W. and D. Gillies (2002). Using Bayesian Networks to Model the Prognosis of Hepatitis C. In 7th Workshop on Intelligent Data Analysis in Medicine and Pharmacology, pages 7.15, Lyon, France

  38. Bang, J. W. and D. Gillies (2002). Using Bayesian Networks with Hidden Nodes to Recognise Neural Cell Morphology. In M. Ishizuka and A. Satter, editors, 7th Pacific Rim International Conference on Arti_cial Intelligence, pages 385.394, Tokyo, New York,. Springer

  39. Bangsø, O. and P. H. Wuillemin (2000). Top-down construction and repetitive structures representation in Bayesian networks. Proceedings of The Thirteenth International Florida Artificial Intelligence Research Symposium Conference. Florida, USA: 282-286

  40. Barahona, P. (1994). "A causal and temporal reasoning model and its use in drug therapy applications." Artif Intell Med 6(1): 1-27

  41. Barker, G. C. (2004). Application of Bayesian Belief Network models to food safety science

  42. Batchelor, C. and J. Cain (1999). "Application of belief networks to water management studies." Agricultural Water Management 40(1): 51-57

  43. Bate, A. (2007). "Bayesian confidence propagation neural network." Drug Saf 30(7): 623-5

  44. Bate, A., M. Lindquist, et al. (1998). "A Bayesian neural network method for adverse drug reaction signal generation." Eur J Clin Pharmacol 54(4): 315-21

  45. Bate, A., M. Lindquist, et al. (2002). "A data mining approach for signal detection and analysis." Drug Saf 25(6): 393-7

  46. Bate, A., M. Lindquist, et al. (2002). "Data-mining analyses of pharmacovigilance signals in relation to relevant comparison drugs." Eur J Clin Pharmacol 58(7): 483-90

  47. Bauer, E., D. Koller, et al. (1997). Update rules for parameter estimation in Bayesian networks. In Geiger D. and Shenoy P. (Eds.) Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers Inc., San Francisco, pp. 3-13

  48. Bayesware. (2007). "Bayesware Knowledge Discovery by Bayesian Networks."

  49. http://www.bayesware.com/.

  50. Beach, B. (1975). "Expert judgment about uncertainty: Bayesian decision making in realistic settings." Organ Behav Hum Perform 14(1): 10-59

  51. Bearfield, G. and W. Marsh (2005). Generalising Event Trees Using Bayesian Networks with a Case Study of Train Derailment. in Proceedings of the 24th International Conference on Computer Safety, Reliability and Security, SAFECOMP 2005, Springer-Verlag, vol. 3688

  52. Beghin, I., A. De Muynck, et al. (1989). "Can the causal model approach contribute to the study of the epidemiology and the control of sleeping sickness?" Ann Soc Belg Med Trop 69 Suppl 1: 31-47; discussion 144

  53. Bellamy, S. L., J. Y. Lin, et al. (2007). "An introduction to causal modeling in clinical trials." Clin Trials 4(1): 58-73

  54. Ben Salem, A., A. Muller, et al. (2006). "Dynamic Bayesian Networks in system reliability analysis in 6th IFAC Symposium on Fault Detection, Supervision and Safety of technical processes." 6th IFAC Symposium on Fault Detection, Supervision and Safety of technical processes, China [hal-00092032 - version 1] (2006-09-08"

  55. Bernardo, J. A. and A. F. Smith (1994). Bayesian Theory, John Wiley and Sons, New York.

  56. Bibi, S. and I. Stamelos (2004). Software Process Modeling with Bayesian Belief Networks. 10th International Software Metrics Symposium (Metrics 2004). Chicago, USA

  57. Biedermann, A., F. Taroni, et al. (2005). "The evaluation of evidence in the forensic investigation of fire incidents. Part II. Practical examples of the use of Bayesian networks." Forensic Science International 147(1): 59-69

  58. Birckmayer, J. D., H. D. Holder, et al. (2004). "A general causal model to guide alcohol, tobacco, and illicit drug prevention: assessing the research evidence." J Drug Educ 34(2): 121-53

  59. Blackburn, J. D., G. D. Scudder, et al. (1996). Improving speed and productivity of software development: a global survey of software developers. IEEE Transactions on Software Engineering, 22(12), 875-885

  60. Blanco, R., I. Inza, et al. (2005). "Feature selection in Bayesian classifiers for the prognosis of survival of cirrhotic patients treated with TIPS." J Biomed Inform 38(5): 376-88

  61. Bobbio, A., L. Portinale, et al. (2001). "Improving the analysis of dependable systems by mapping fault trees into Bayesian networks." Reliability Engineering and System Safety 71(3): 249-260

  62. Bockhorst, J., M. Craven, et al. (2003). "A Bayesian network approach to operon prediction." Bioinformatics 19(10): 1227-35

  63. Boer, R., S. Plevritis, et al. (2004). "Diversity of model approaches for breast cancer screening: a review of model assumptions by the Cancer Intervention and Surveillance Network (CISNET) Breast Cancer Groups." Stat Methods Med Res 13(6): 525-38

  64. Borsuk, M. E., C. A. Stow, et al. (2004). "A Bayesian network of eutrophication models for synthesis, prediction, and uncertainty analysis." Ecological Modelling 173(2-3): 219-39

  65. Bothtner, U., S. E. Milne, et al. (2002). "Bayesian probabilistic network modeling of remifentanil and propofol interaction on wakeup time after closed-loop controlled anesthesia." J Clin Monit Comput 17(1): 31-6

  66. Bottcher, P., R. Stoddard, et al. (1995). A Bayesian approach for modeling quality in software products and processes. Proc 5th Int Conf Software Quality, 214-253

  67. Bouckaert, R. R. (1996). A Stratified Simulation Scheme for Inference in Bayesian Belief Networks. Utrecht University, Department of Computer Science, P.O.Box 80.089 3508 TB Utrecht, The Netherlands, email: remco@cs.ruu.nl

  68. Boudali, H. and J. B. Dugan (2005). "A discrete-time Bayesian network reliability modeling and analysis framework." Reliability Engineering and System Safety 87(3): 337-349

  69. Boudali, H. and J. B. Dugan (2006). "A Continuous-Time Bayesian Network Reliability Modeling and Analysis framework." IEEE Transactions on Reliability 55: 86-97

  70. Bouissou, M., F. Martin, et al. (1999). "Assessment of a Safety-Critical System Including Software: A Bayesian Belief Network for Evidence Sources." "Free. Ann. Reliability and Maintainability Symp., RAMS"

  71. Boutilier, C., N. Friedman, et al. (1996). "Context-specific independence in Bayesian networks." "In Proc. 12th UAI, pages 115-123"

  72. Bradford, J., C. Needham, et al. (2006). "Insights into protein-protein interfaces using a Bayesian network prediction method." J Mol Biol 362(2): 365-86

  73. Brage, D. and W. Meredith (1994). "A causal model of adolescent depression." J Psychol 128(4): 455-68

  74. Brewer, M. J. (2003). "Discretisation for inference on Bayesian mixture models." "Statistics and Computing 13, 209-219"

  75. Brown, L. E., I. Tsamardinos, et al. (2004). "A novel algorithm for scalable and accurate Bayesian network learning." Medinfo 11(Pt 1): 711-5

  76. Bryan, B. and M. Garrod (2006). Combining rapid field assessment with a Bayesian network to prioritise investment in watercourse protection, CSIRO Land and Water Science Report 10/06, April, www.clw.csiro.au/publications/science/2006/sr10-06.pd

  77. Bulashevska, S., O. Szakacs, et al. (2004). "Pathways of urothelial cancer progression suggested by Bayesian network analysis of allelotyping data." Int J Cancer 110(6): 850-6

  78. Burden, F. R. and D. A. Winkler (2005). "Predictive Bayesian neural network models of MHC class II peptide binding." J Mol Graph Model 23(6): 481-9

  79. Burge, J., T. Lane, et al. (2007). "Discrete dynamic Bayesian network analysis of fMRI data." Hum Brain Mapp

  80. Burnside, E., D. Rubin, et al. (2006). "Bayesian network to predict breast cancer risk of mammographic microcalcifications and reduce number of benign biopsy results: initial experience." Radiology 240(3): 666-73

  81. Burnside, E., D. Rubin, et al. (2000). "A Bayesian network for mammography." Proc AMIA Symp: 106-10

  82. Burnside, E., D. Rubin, et al. (2004). "Using a Bayesian network to predict the probability and type of breast cancer represented by microcalcifications on mammography." Medinfo 11(Pt 1): 13-7

  83. Burnside, E. S. (2005). "Bayesian networks: computer-assisted diagnosis support in radiology." Acad Radiol 12(4): 422-30

  84. Burnside, E. S., D. L. Rubin, et al. (2006). "Bayesian network to predict breast cancer risk of mammographic microcalcifications and reduce number of benign biopsy results: initial experience." Radiology 240(3): 666-73

  85. Burnside, E. S., D. L. Rubin, et al. (2004). "Using a Bayesian network to predict the probability and type of breast cancer represented by microcalcifications on mammography." Medinfo 11(Pt 1): 13-7

  86. Burnside, E. S., D. L. Rubin, et al. (2004). "A probabilistic expert system that provides automated mammographic-histologic correlation: initial experience." AJR Am J Roentgenol 182(2): 481-8

  87. Buxton, H. (1997). "Advanced visual surveillance using Bayesian networks." "COLLOQUIUM DIGEST- IEE, , ISSUE 74, pages"

  88. Call, C. and P. Gonsalves (2006). Belief Network-based Situation Assessment for Air Operations Centers. Proceedings of SPIE Defense & Security, Orlando, FL.

  89. Campos, L. M. d., J. A. Gámez, et al. (2001). "Accelerating chromosome evaluation for partial abductive inference in Bayesian networks by means of explanation set absorption." International Journal of Approximate Reasoning, 27(2): 121-142

  90. Canol, R., C. Sordo, et al. (2004). Applications of Bayesian Networks in Meteorology. Advances in Bayesian Networks. Gamez, Springer: 309-327.

  91. Card, D. (1998). Learning from our mistakes with defect causal analysis. IEEE Software, 15(1), 56-63

  92. Castillo, E., J. M. Gutierrez, et al. (1997). Sensitivity analysis in discrete Bayesian networks. IEEE Transactions on Systems, Man and Cybernetics, Part A, Volume: 27, Issue: 4 , July, 412 - 423

  93. Chakraborty, S., M. Ghosh, et al. (2005). "Bayesian neural networks for bivariate binary data: an application to prostate cancer study." Stat Med 24(23): 3645-62

  94. Chan, H. and A. Darwiche (2005). "On the Revision of Probabilistic Beliefs Using Uncertain Evidence." Artificial Intelligence 163(67-90)

  95. Chang, J., K. Hwang, et al. (2005). "Bayesian network learning with feature abstraction for gene-drug dependency analysis." J Bioinform Comput Biol 3(1): 61-77

  96. Chang, K. C. and Fung (1997). "Target identification with Bayesian networks in a multiple hypothesis tracking system." OPT. Eng 36(3): 684-691

  97. Charniak, E. (1991). "Bayesian Networks without tears." AI Magazine, AAAI Winter: 50-6

  98. Chavira, M. and A. Darwiche (2007). Compiling Bayesian Networks Using Variable Elimination. 20th International Joint Conference on Artificial Intelligence (IJCAI). Hyderabad, India

  99. Chavira, M., A. Darwiche, et al. (2006). "Compiling Relational Bayesian Networks for Exact Inference." International Journal of Approximate Reasoning (IJAR) 42: 4-20

  100. Chen, R. and E. H. Herskovits (2007). "Clinical diagnosis based on bayesian classification of functional magnetic-resonance data." Neuroinformatics 5(3): 178-88

  101. Chen, X., M. Chen, et al. (2006). "BNArray: an R package for constructing gene regulatory networks from microarray data by using Bayesian network." Bioinformatics 22(23): 2952-4

  102. Cheng, J. (2001). Belief Network Powersoft System, University of Alberta, http://www.cs.ualberta.ca/~jcheng/bnsoft.ht

  103. Cheng, J., D. A. Bell, et al. (1997). "An Algorithm for Bayesian Belief Network Construction from Data." http://www.cs.ualberta.ca/~jcheng/Doc/aistat97.pdf.

  104. Cheng, P. W. and L. R. Novick (1990). "A probabilistic contrast model of causal induction." J Pers Soc Psychol 58(4): 545-67

  105. Chickering, D. M. (1996). Learning Bayesian Networks is NP-Complete. LECTURE NOTES IN STATISTICS SPRINGER VERLAG. 112: 121-130.

  106. Chickering, D. M. and D. Heckerman (1997). ".Efficient, Approximations for the Marginal Likelihood of Bayesian Networks with Hidden Variables." MACHINE LEARNING 29(2/3): 181-212

  107. Chien, C.-F., S.-L. Chen, et al. "Using Bayesian network for fault location on distribution feeder." IEEE Trans Power Delivery 17(13): 785- 793

  108. Chulani, S., B. Boehm, et al. (1998). Calibrating Software Cost Models Using Bayesian Analysis. USC-CSE 1998

  109. Chulani, S., B. Boehm, et al. (1999). Bayesian analysis of empirical software engineering cost models. IEEE Transactions on Software Engineering, 25(4), 573-583

  110. Chung, L., A. W. Pan, et al. (2003). "A causal model of rehabilitation resource use for subjects with spinal cord injury in Taiwan." J Rehabil Med 35(5): 208-12

  111. Clarke, S. R., M. Bailey, et al. (2008). "Successful applications of statistical modeling to betting markets." Mathematics Today (Bulletin of the IMA) 44(1): 38-44

  112. Cobb, B. and P. Shenoy (2005). "On the plausibility transformation method for translating belief function models to probability models." Int J Approx Reason 41(3): 314-40

  113. Cofi˜no, A. S., R. Cano, et al. (2002). Bayesian networks for probabilistic weather prediction. Proceedings of the 15th European Conference on Artificial Intelligence: 695-700

  114. Coolen, F. P., M. Goldstein, et al. (2007). "Using Bayesian statistics to support testing of software." Journal of Risk and Reliability 221(1): 85-93

  115. Cooper, G. F. and E. Herskovits (1992). A Bayesian Method for the Induction of Probabilistic Networks from Data. Machine Learning, 9, Page 309

  116. Cooper, N., A. Sutton, et al. (2002). "Decision analytical economic modelling within a Bayesian framework: application to prophylactic antibiotics use for caesarean section." Stat Methods Med Res 11(6): 491-512

  117. Cooper., G. F. (1990). "The computational complexity of probabilistic inference using bayesian belief networks ." Artificial Intelligence 42(2-3): 393-405

  118. Coulter, D. M., A. Bate, et al. (2001). "Antipsychotic drugs and heart muscle disorder in international pharmacovigilance: data mining study." BMJ 322(7296): 1207-9

  119. Coupe, V. M., N. Peek, et al. (1999). "Using sensitivity analysis for efficient quantification of a belief network." Artif Intell Med 17(3): 223-47

  120. Courtois, P. J., N. E. Fenton, et al. (1998). Examination of bayesian belief network for safety assessment of nuclear computer-based systems, City University, Centre for Software Reliability

  121. Cowell, R., S. Lauritzen, et al. (2006). "Identification and separation of DNA mixtures using peak area information." Forensic Science International 166(1): 28-34

  122. Cowell, R. G. (2003). "Finex: a Probabilistic Expert System for forensic identification." Forensic Science International 134(2): 196-206

  123. Cowell, R. G., A. P. Dawid, et al. (1991). "A Bayesian expert system for the analysis of an adverse drug reaction." Artificial Intelligence in Medicine 3: 257-270

  124. Cowell, R. G., A. P. Dawid, et al. (1999). Probabilistic Networks and Expert Systems. New York, Springer

  125. Cowell, R. G., A. P. Dawid, et al. (1993). "Sequential Model Criticism in Probabilistic Expert Systems." IEEE Transactions on Pattern Analysis and Machine Intelligence 15(3): 209-219

  126. Cox, Z. and J. Pfautz (2007). Causal Influence Models: A Method for Simplifying Construction of Bayesian Networks (Rep. No. R-BN07-01). Cambridge, MA: Charles River Analytics Inc

  127. Croft, J. and J. Q. Smith (2003). "Discrete mixtures in simple Bayesian Networks with hidden variables." J of Computational Statistics and Data Analysis 41(3-4): 539-547

  128. Cruz-Ramirez, N., H. G. Acosta-Mesa, et al. (2007). "Diagnosis of breast cancer using Bayesian networks: A case study." Comput Biol Med 37(11): 1553-64

  129. D’Ambrosio, B. (1999). "Inference in Bayesian Networks." AI Magazine, AAAI 20(2): 21-36

  130. Dagum, P. and R. M. Chavez (1993). Approximating Probabilistic Inference in Bayesian Belief Networks. Pattern Analysis and Machine Intelligence 15(3):246-255

  131. Dagum, P. and A. Galper (1995). "Time series prediction using belief network models." International Journal of Human-Computer Studies 42(6): 617-632

  132. Dahll, G. (2000). "Combining disparate sources of information in the safety assessment of software-based systems." Nuclear Engineering and Design 195(3): 307-319


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