References

  • S. K. Andersen, K. G. Olesen, F. V. Jensen & F. Jensen (1989). Hugin – a shell for building Bayesian belief universes for expert systems. In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, pages 1080-1085, Detroit, Michigan, Aug. 20-25.

  • I. Beinlich, H. Suermondt, R. Chavez & G. Cooper (1989). The ALARM monitoring system: A case study with two probabilistic inference techniques for belief networks. Proceedings of the second European Conference on Artificial Intelligence in Medicine, pp 247-256.

  • B. Boerlage (1992). Link Strength in Bayesian Networks. MSc thesis, Department of Computer Science, University of British Columbia, Canada. Also Tech. Report 94-17, Department of Computer Science, University of British Columbia, Canada.

  • O. Cappe & E. Moulines. Online em algorithm for latent data models. Journal of the Royal Statistical Society Series B (Statistical Methodology), 71(3):593-613, 2009.

  • G. Cooper (1984). NESTOR: A computer-based medical diagnostic aid that integrates causal and probabilistic knowledge. PhD thesis, Medical Information Sciences, Stanford University, Stanford, CA.

  • R. G. Cowell & A. P. Dawid (1992). Fast retraction of evidence in a probabilistic expert system. Statistics and Computing, 2:37-40.

  • A. P. Dawid (1992). Applications of a general propagation algorithm for probabilistic expert systems. Statistics and Computing, 2:25-36.

  • D. Heckerman, J. Breese & K. Rommelse (1994). Troubleshooting under Uncertainty. Technical report msr-tr-94-07. Microsoft Research, Advanced Technology Division, Microsoft Corporation.

  • F. Jensen (1994). Implementation aspects of various propagation algorithms in Hugin. Research Report R-94-2014, Department of Mathematics and Computer Science, Aalborg University, Denmark.

  • F. Jensen & S. K. Andersen (1990). Approximations in Bayesian belief universes for knowledge-based systems. In Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence, pages 162-169, Cambridge, Massachusetts, July 27-29.

  • F. Jensen, F. V. Jensen & S. L. Dittmer (1994). From influence diagrams to junction trees. In R. L. de Mantaras and D. Poole, editors, Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, pages 367-373, Seattle, Washington, July 29-31. Morgan Kaufmann, San Mateo, California.

  • F. V. Jensen, S. L. Lauritzen & K. G. Olesen (1990(1)). Bayesian updating in causal probabilistic networks by local computations. Computational Statistics Quarterly, 4:269-282.

  • F. V. Jensen, K. G. Olesen & S. K. Andersen (1990(2)). An algebra of Bayesian belief universes for knowledge-based systems. Networks, 20(5):637-659. Special Issue on Influence Diagrams.

  • F. V. Jensen, B. Chamberlain, T. Nordahl & F. Jensen (1991). Analysis in Hugin of data conflict. In P. P. Bonissone, M. Henrion, L. N. Kanal, and J. F. Lemmer, editors, Uncertainty in Artificial Intelligence, volume 6, pages 519-528. Elsevier Science Publishers, Amsterdam, The Netherlands.

  • F. V. Jensen (2001). Bayesian Networks and Decision Graphs, Springer.

  • F. V. Jensen (1996). An Introduction to Bayesian Networks, Springer.

  • U. Kjærulff (1990). Triangulation of graphs - algorithms giving small total state space. Research Report R-90-09. Department of Mathematics and Computer Science, Aalborg University, Denmark.

  • S. L. Lauritzen (1992). Propagation of probabilities, means, and variances in mixed graphical models. Journal of the American Statistical Association (Theory and Methods), 87(420):1098-1108.

  • S. L. Lauritzen (1995). The EM algorithm for graphical association models with missing data. Computational Statistics & Data Analysis, 19:191-201.

  • S. L. Lauritzen, A. P. Dawid, B. N. Larsen & H.-G. Leimer (1990). Independence properties of directed Markov fields. Networks, 20(5):491-505. Special Issue on Influence Diagrams.

  • S. L. Lauritzen and D. Nilsson. Representing and solving decision problems with limited information. Management Science, 47(9):1235–1251, Sept. 2001.

  • S. L. Lauritzen & D. J. Spiegelhalter (1988). Local computations with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society, Series B (Methodological), 50(2):157-224.

  • J. Matheson (1990). Using Influence diagrams to value information and control. Influence Diagrams, Belief Networks and Decision Analysis.

  • R. Neapolitan (1990). Probabilistic Reasoning in Expert Systems: Theory and Algorithms. John Wiley & Sons, New York.

  • K. G. Olesen, S. L. Lauritzen & F. V. Jensen (1992). aHugin: A system creating adaptive causal probabilistic networks. In D. Dubois, M. P. Wellman, B. D’Ambrosio, and P. Smets, editors, Proceedings of the Eighth Conference on Uncertainty in Artificial Intelligence, pages 223-229, Stanford, California, July 17-19. Morgan Kaufmann, San Mateo, California.

  • J. Pearl (1988). Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Mateo, CA.

  • J. Pearl (2000). Causality: Models, Reasoning, and Inference. Cambridge University Press, UK.

  • D. Poole & E. Neufeld (1988). Sound probabilistic inference in Prolog: An executable specification of influence diagrams.

  • R. Qi (1994). Decision graphs: Algorithms and applications to influence diagram evaluation and high-level path planning under uncertainty. PhD thesis, Department of Computer Science, University of British Columbia, Canada. Also Tech. Report 94-27, Department of Computer Science, University of British Columbia, Canada.

  • H. Raiffa (1968). Decision Analysis, Introductory Lectures on Choices under Uncertainty. Addison-Wesley, Reading, Massachusetts.

  • L. K. Rasmussen (1995(1)). Bayesian network for blood typing and parentage verification of cattle. Dina research report no. 38. Department of Mathematics and Computer Science, Aalborg University, Denmark.

  • L. K. Rasmussen (1995(2)). BOBLO: an expert system based on Bayesian networks to blood group determination of cattle. Research report 16. Research Center Foulum, Denmark, PB 23, 8830 Tjele, Denmark.

  • J. Smith, S. Holtzman & J. Matheson (1993). Structuring conditional relationships in influence diagrams Operations Research, 41(2):280-297.

  • D. J. Spiegelhalter & S. L. Lauritzen. Sequential updating of conditional probabilities on directed graphical structures. Networks, 20(5):579-605, Aug. 1990. Special Issue on Influence Diagrams.

  • P. Spirtes, C. Glymour & R. Scheines (2000). Causation, Prediction, and Search. MIT Press, Adaptive Computation and Machine Learning, second edition.

  • L. Zhang (1993). A computational theory of decision networks. PhD thesis, Department of Computer Science, University of British Columbia, Canada. Also Tech. Report 94-8, Department of Computer Science, University of British Columbia, Canada.

  • S. L. Lauritzen and D. Nilsson. (2001) Representing and solving decision problems with limited information. Management Science, 47(9):1235 - 1251, Sept. 2001.