References
Applications
[GHVW98] Grois, E., Hsu, W. H., Voloshin, M., & Wilkins, D. C. (1998). Bayesian Network Models for Automatic Generation of Crisis Management Training Scenarios. In Proceedings of the Tenth Innovative Applications of Artificial Intelligence Conference (IAAI-98), Madison, WI, pp. 1113-1120. Menlo Park, CA: AAAI Press. (PDF / PostScript / .ps.gz)
General
[Br95] Brooks, F. P. (1995). The Mythical-Man Month, 20th Anniversary Edition: Essays on Software Engineering. Boston, MA: Addison-Wesley.
[La00] Langley, P. (2000). Crafting papers on machine learning. In Proceedings of the Seventeenth International Conference on Machine Learning, Stanford, CA, pp. 1207-1211. San Francisco, CA: Morgan Kaufmann Publishers. (HTML / .ps.gz)
[La02] Langley, P. (2002). Issues in Research Methodology. Palo Alto, CA: Institute for the Study of Learning and Expertise. Available from URL: http://www.isle.org/~langley/methodology.html.
Recent and Current Research
[FGKP99] Friedman, N., Getoor, L., Koller, D., & Pfeffer, A. (1999). Learning Probabilistic Relational Models. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI-1999), Stockholm, SWEDEN. San Francisco, CA: Morgan Kaufmann Publishers. (PDF)
[GFTK02] Getoor, L., Friedman, N., Koller, D., & Taskar, B. (2002). Learning Probabilistic Models of Link Structure. Journal of Machine Learning Research, 3(2002):679-707. (PDF)
[GH02] Guo, H. & Hsu, W. H. (2002). A Survey of Algorithms for Real-Time Bayesian Network Inference. In Guo, H., Horvitz, E., Hsu, W. H., and Santos, E., eds. Working Notes of the Joint Workshop (WS-18) on Real-Time Decision Support and Diagnosis, AAAI/UAI/KDD-2002. Edmonton, Alberta, CANADA, 29 July 2002. Menlo Park, CA: AAAI Press. (PDF)
[Gu02] Guo, H. (2002). A Bayesian Metareasoner for Algorithm Selection for Real-time Bayesian Network Inference Problems (Doctoral Consortium Abstract). In Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI-2002), Edmonton, Alberta, CANADA, p. 983. Menlo Park, CA: AAAI Press. (PDF)
[HGPS02] Hsu, W. H., Guo, H., Perry, B. B., & Stilson, J. A. (2002). A permutation genetic algorithm for variable ordering in learning Bayesian networks from data. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002), New York, NY. San Francisco, CA: Morgan Kaufmann Publishers. (PDF / PostScript / .ps.gz) - Nominated for Best of GECCO-2002, Genetic Algorithms Deme (31 nominees, 160 accepted papers out of 320)
Software
[SGS04] Spirtes, P. & Glymour, C. & Scheines, R. (2004). The TETRAD Project: Causal Models and Statistical Data. Pittsburgh, PA: Carnegie Mellon University Department of Philosophy. Available from URL: http://www.phil.cmu.edu/projects/tetrad/.
[LBB+04] Lauritzen, S. L. & Badsberg, J. H. & Bøttcher, S. G. & Dalgaard, P. & Dethlefsen, C. & Edwards, D. & Eriksen, P. S. & Gregersen, A. R. & Højsgaard, S. & Kreiner, S. (2004). gR - Graphical models in R. Aalborg, DENMARK: Aalborg University Department of Mathematical Sciences. Available from URL: http://www.math.auc.dk/gr/.
[Mu04] Murphy, K. P. (2004). Bayes Net Toolbox v5 for MATLAB. Cambridge, MA: MIT Computer Science and Artificial Intelligence Laboratory. Available from URL: http://www.ai.mit.edu/~murphyk/Software/BNT/bnt.html.
[PS02] Perry, B. P. & Stilson, J. A. (2002). BN-Tools: A Software Toolkit for Experimentation in BBNs (Student Abstract). In Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI-2002), Edmondon, Alberta, CANADA, pp. 963-964. Menlo Park, CA: AAAI Press. (PS)
Textbooks and Tutorials
[Mu01] Murphy, K. P. (2001). A Brief Introduction to Graphical Models and Bayesian Networks. Berkeley, CA: Department of Computer Science, University of California - Berkeley. Available from URL: http://www.cs.berkeley.edu/~murphyk/Bayes/bayes.html.
[Ne90] Neapolitan, R. E. (1990). Probabilistic Reasoning in Expert Systems: Theory and Applications. New York, NY: Wiley-Interscience. (Out of print; Amazon.com reference)
[Ne03] Neapolitan, R. E. (2003). Learning Bayesian Networks. Englewood Cliffs, NJ: Prentice Hall. (Amazon.com reference)
Foundational Material and Seminal Research
[CH92] Cooper, G. F. & Herskovits, E. (1992). A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9(4):309-347.
[Jo98] Jordan, M. I., ed. (1998). Learning in Graphical Models. Cambridge, MA: MIT Press. (Amazon.com reference)
[LS88] Lauritzen, S., & Spiegelhalter, D. J. (1988). Local Computations with Probabilities on Graphical Structures and Their Application to Expert Systems. Journal of the Royal Statistical Society Series B 50:157-224.
[SGS01] Spirtes, P. & Glymour, C. & Scheines, R. (2001). Causation, Prediction, and Search, Second Edition. Cambridge, MA: MIT Press. (Amazon.com reference)
Theses and Dissertations Related to BNJ
[Me99] Mengshoel, O. J. (1999). Efficient Bayesian Network Inference: Genetic Algorthms, Stochastic Local Search and Abstraction. Ph.D. Dissertation, Department of Computer Science, University of Illinois at Urbana-Champaign, May, 1999. Available from URL: http://www-kbs.ai.uiuc.edu/web/kbs/publicLibrary/KBSPubs/Thesis/.
Workshops Relevant to BNJ
[GHHS02] Guo, H., Horvitz, E., Hsu, W. H., and Santos, E., eds. (2002). Working Notes of the Joint Workshop (WS-18) on Real-Time Decision Support and Diagnosis, AAAI/UAI/KDD-2002. Edmonton, Alberta, CANADA, 29 July 2002. Menlo Park, CA: AAAI Press. Available from URL: http://www.kddresearch.org/Workshops/RTDSDS-2002.
[HJP03] Hsu, W. H., Joehanes, R., & Page, C. D. (2003). Working Notes of the Workshop on Learning Graphical Models in Computational Genomics, International Joint Conference on Artificial Intelligence (IJCAI-2003). Acapulco, MEXICO, 09 Aug 2003. Available from URL: http://www.kddresearch.org/Workshops/IJCAI-2003-Bioinformatics.
Page created: Fri 20
Dec 2002
Last updated: Sun 06 Jun 2004
William H. Hsu
BNJ Development Team