Publications of Uffe Kjærulff

• Kjærulff, U. B. and Madsen, A. L. (2008), Bayesian Networks and Influence Diagrams --- A Guide to Construction and Analysis, 318 pages, Springer.
• Madsen, A. L., Kalwa, J., and Kjærulff, U. B. (2007), Risk Management in Robotics, In Patrick Naim, Olivier Pourret, and Bruce Marcot(eds), Bayesian Belief Networks: A Practical Guide to Applications, Wiley & Sons.
• Madsen, A. L. and Kjærulff, U. B. (2007), Applications of HUGIN to Diagnosis and Control of Autonomous Vehicles, In Peter J. F. Lucas, Jose A. Gamez, and Antonio Salmeron (eds), Advances in probabilistic graphical models, Vol. 213 of Studies in fuzziness and soft computing, Springer, 313--332.
• Kjærulff, U. B. and Madsen, A. L. (2005), Probabilistic Networks - An Introduction to Bayesian Networks and Influence Diagrams, 133 pages, Unpublished.
• Madsen, A. L., Jensen, F., Kjærulff, U. B., Lang, M. (2005), The Hugin Tool for Probabilistic Graphical Models, International Journal of Artificial Intelligence Tools, 14(3):507-543.
• Madsen, A. L., Kjærulff, U. B., Kalwa, J., Perrier, M., Sotelo, M. A. (2004), Applications of probabilistic graphical models to diagnosis and control of autonomous vehicles. The Second Bayesian Modeling Applications Workshop (during UAI-2004).
• Kjærulff, U. B. and Madsen, A. L. (2004), A methodology for acquiring qualitative knowledge for probabilistic graphical models. In Proceedings of the 10th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2004), pp. 143-150.
• Castillo, E. and Kjærulff, U. B. (2003), Sensitivity analysis in Gaussian Bayesian networks using a symbolic-numerical technique. Reliability Engineering and System Safety, 79(2):139-148.
• Madsen, A. L., Lang, M., Kjærulff, U. B. and Jensen, F. (2003), The Hugin Tool for Learning Bayesian Networks. In Nielsen, T. D. and Zhang, N. L. (eds.), Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 7th European Conference, ECSQARU 2003. Lecture Notes in Computer Science, pp. 594-605, Springer-Verlag Heidelberg.
• Jensen, F., Kjærulff, U. B., Lang, M. and Madsen, A. L. (2002), HUGIN --- The Tool for Bayesian Networks and Influence Diagrams. In Gámez, J. A. and Salmerón, A. (eds.), In Proceedings of the First European Workshop on Probabilistic Graphical Models (PGM'02), pp. 212-221.
• Castillo, E., Kjærulff, U. B., and van der Gaag, L. C. (2001), Sensitivity Analysis in Gaussian Networks. Third International Symposium on Sensitivity Analysis of Model Output.
• Jensen, F. V., Kjærulff, U., Kristiansen, B., Langseth, H., Skaanning, C., Vomlel, J. and Vomlelová, M. (2001), The SACSO methodology for troubleshooting complex systems. Artificial Intelligence for Engineering Design, Analysis and Manufacturing (AIEDAM), 15(4):321-333.
• Jensen, F. V., Skaanning, C., Kjærulff, U. (2001), The SACSO System for Troubleshooting of Printing Systems. In Proceedings of the Seventh Scandinavian Conference on Artificial Intelligence.
• Kjærulff, U. and van der Gaag, L. C. (2000), Making Sensitivity Analysis Computationally Efficient, In Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence, pp. 317-325, Morgan Kaufmann, San Francisco, California.
• Nielsen, T. D., Wuillemin, P.-H., Jensen, F. V., Kjærulff, U. (2000), Using ROBDDs for inference in Bayesian networks with troubleshooting as an example, To appear in Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, California.
• Coupe, V. M. H., Jensen, F. V., Kjærulff, U. and van der Gaag, L. C. (2000), A Computational Architecture for N-way Sensitivity Analysis of Bayesian Networks. Technical Report.
• Skaanning, C., Jensen, F. V., Kjærulff, U., and Madsen, A. L. (1999), Acquisition and transformation of likelihoods to conditional probabilities for Bayesian networks, AAAI Spring Symposium, Stanford, USA.
• Skaanning, C., Jensen, F. V., Kjærulff, U., Pelletier, P, Rostrup-Jensen, L. (1998), Printing System Diagnosis - A Bayesian Network Application, Ninth International Workshop on Principles of Diagnosis (Dx98), Sea Crest Resort, Cape Cod, Massachusetts, USA.
• Kjærulff, U. (1998), Inference in Bayesian Networks Using Nested Junction Trees , In M. I. Jordan (Ed.), Learning in Graphical Models, Kluwer Academic Press.
• Kjærulff, U. (1997), Nested Junction Trees , Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Francisco, California.
• Jensen, C. S., Kong A., Kjærulff, U (1995), Blocking Gibbs Sampling in Very Large Probabilistic Expert Systems, International Journal of Human-Computer Studies, 42:647-666.
• Dawid, A. P. Kjærulff, U, Lauritzen, S. L. (1995), Hybrid Propagation in Junction Trees, In Advances in Intelligent Computing, B. Bouchon-Meunier and R. R. Yager and L. A. Zadeh (eds), 945:87-97, Lecture Notes in Computer Science, Springer-Verlag.
• Kjærulff, U. (1995), dHugin: A Computational System for Dynamic Time-Sliced Bayesian Networks, International Journal of Forecasting, Special Issue on Probability Forecasting, 11:89-111.
• Kjærulff, U. (1995), HUGS: Combining Exact Inference and Gibbs Sampling in Junction Trees , Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, 368-375, Morgan Kaufmann, San Francisco, California.
• Kjærulff, U. (1994), Reduction of Computational Complexity in Bayesian Networks through Removal of Weak Dependences, Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, 374-382, Morgan Kaufmann, San Francisco, California.
• Kjærulff, U. (1993), Approximation of Bayesian networks through edge removals, Research Report IR-93-2007, Dept. of Mathematics and Computer Science, Aalborg University.
• Kjærulff, U. (1993), Aspects of Efficiency Improvement in Bayesian Networks, PhD thesis, Dept. of Mathematics and Computer Science, Aalborg University.
• Kjærulff, U. (1992), A computational scheme for reasoning in dynamic probabilistic networks , Proceedings of the Eighth Conference on Uncertainty in Artificial Intelligence, 121-129, Morgan Kaufmann, San Francisco.
• Kjærulff, U. (1992), Optimal Decomposition of Probabilistic Networks by Simulated Annealing , Statistics and Computing, 2:7-17.
• Kjærulff, U (1990), Triangulation of Graphs --- Algorithms Giving Small Total State Space , Research Report R-90-09, Dept. of Mathematics and Computer Science, Aalborg University.

Slides

• Tutorial on Inference in Bayesian Networks, Summer school on Learning in Graphical Models, Ettore Majorana Centre for Scientific Culture, Erice, Sicily, 1996.

Miscellaneous

• Kjærulff, U., Jensen, F.V. (1996), Bayesian networks. (A brief introduction.)