Huginの製品・サービスはマインドウエア総研により翻訳・提供されています。pict

Publications

Books

Book Chapters

2008

  • Weidl, G., Madsen, A. L. and Dahlquist, E. (2008). Complex industrial process operation, in O. Pourret, P. Naim, B. Marcot (eds), Bayesian Networks: A Practical Guide to Applications, Wiley, pp. 313-328.
  • Ejsing, E., Vastrup, P. and Madsen, A. L. (2008). Probability of default for large corporates, in O. Pourret, P. Naim, B. Marcot (eds), Bayesian Networks: A Practical Guide to Applications, pp. 329-344.
  • Madsen, A. L., Kalwa, J. and Kjærulff, U. B. (2008). Risk Management in Robotics, in O. Pourret, P. Naim, B. Marcot (eds), Bayesian Networks: A Practical Guide to Applications, pp. 345-363.

2007

  • Madsen, A. L. & Kjærulff, U. B. (2007). Applications of HUGIN to diagnosis and control of autonomous vehicles, in P. Lucas, J. A. Gamez & A. Salmeron (eds), Advances in probabilistic graphical models, Vol. 213 of Studies in fuzziness and soft computing, Springer, pp. 313­-332.

Scientific Articles

2013

  • Madsen, A. L. and Butz, C. (2013). Ordering arc-reversal operations when eliminating variables in lazy AR propagation. Int. J. Approx. Reason., Vol 54 (8), pp 1182-1196, doi:10.1016/j.ijar.2013.02.007.
  • Madsen, A. L. and Butz, C. (2013). On the Tree Structure used by Lazy Propagation for Inference in Bayesian Networks. In Proceedings of The Twelfth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, pages 400-411
  • Butz, C., Yan, W. and Madsen, A. L. (2013). On Semantics of Inference in Bayesian Networks. In Proceedings of The Twelfth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, pages 73-84.
  • Butz, C., Yan, W. and Madsen, A. L. (2013). d-Separation: Strong Completeness of Semantics in Bayesian Network Inference. In Proceedings of The Twenty-sixth Canadian Conference on Artificial Intelligence, pages 13-24.
  • Cabanas, R.,  Cano, A.,  Gomez-Olmedo, M. and Madsen, A. L. (2013). Approximate Lazy Evaluation of Influence Diagrams. In Proceedings of the 15th Conference of the Spanish Association for Artificial Intelligence, CAEPIA, pages 321-331.
  • Garcia, A. B., Madsen, A. L. and Vigre, H. Integration of Epidemiological Evidence in a Decision Support Model for the Control of Campylobacter in Poultry Production (2013), Agriculture, 3(3), 516-535
  • Rasmussen, S., Madsen, A. L. and Lund, M. (2013). A Bayesian network as a modelling tool for risk management in agriculture. Presented at EAAE 2013. No 2013/12, IFRO Working Paper from University of Copenhagen, Department of Food and Resource Economics.

2012

  • Madsen, A. L. and Butz, C. (2012). On the Importance of Elimination Heuristics in Lazy Propagation in Proceedings of the 6th European Workshop on Probabilistic Graphical Models, pp 227-234.
  • Garcia, A. B., Madsen, A.L., and Vigre, H. (2012). The use of Probabilistic Graphical Models to develop a cost-effective vaccination strategy against Campylobacter in poultry. Abstract and poster presentation at the 13th Conference of the International Society for Veterinary Epidemiology and Economics (ISVEE XIII).
  • Madsen, A. L., Karlsen, M., Barker, G. C., Garcia, A. B., Hoorfar, J., Jensen, F (2012). An Architecture For Web Deployment Of Decision Support Systems Based On Probabilistic Graphical Models With Applications. Technical Report TR_12_001, Aalborg University.

2011

  • Butz, C., Madsen, A. L., and Williams, K. (2011). Using Four Cost Measures to Determine Arc Reversal Orders, Proceedings of The Eleventh European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, pp 110-121.

2010

2008

  • Madsen, A. L. (2008). Belief update in CLG Bayesian networks with lazy propagation, Int. J. Approx. Reason., Vol 49 (2), pp 503-521.
  • Ferrara, L., Mårtenson, C., Svenson, P., Svensson, P., Hidalgo, J., Molano, A., and Madsen, A. L. (2008). Integrating Data Sources and Network Analysis Tools to Support the Fight Against Organized Crime. Intelligence and Security Informatics, Lecture Notes in Computer Science, Springer, pp. 171-182.
  • Madsen, A. L. (2008). Solving CLQG Influence Diagrams Using Arc-Reversal Operations in a Strong Junction Tree, in Proceedings of the 4th European Workshop on Probabilistic Graphical Models, pp. 201-208.
  • Madsen, A. L. (2008). New Methods for Marginalization in Lazy Propagation, in Proceedings of the 4th European Workshop on Probabilistic Graphical Models, pp. 193-200.

2007

  • Henriksen, H. J., Rasmussen, P., Brandt, G., von Bülow, D., Jensen, F. V. (2007). Bayesian Networks as a Participatory Modelling Tool for Ground Water Protection. Topics on System Analysis and Integrated Water Resource Management. Elsevier, 2007. 27 s.

2006

  • Madsen, A. L. (2006). Belief Update in CLG Bayesian Networks With Lazy Propagation, Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, pages 306-313.
  • Madsen, A. L. (2006), Variations Over the Message Computation Algorithm of Lazy Propagation, IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, 36 (3), pages: 636-648.
  • Olesen, K.G., Hejlesen, O.K., Dessau, R., Beltoft, I. and Trangeled, M. (2006): Diagnosing Lyme Disease Tailoring patient specific Bayesian networks for temporal reasoning. In Proceedings of the Third European Workshop on Probabilistic Graphical Models (PGM ¡06), Prague, Czech Republic. ISBN 80-86742-14-8.

2005

  • Weidl, G., Madsen, A. L. and S. Israelson, S. (2005). Applications of object-oriented Bayesian networks for condition monitoring, root cause analysis and decision support on operation of complex continuous processes, Computers and Chemical Engineering, 29, pages 1996–2009.
  • Madsen, A. L. (2005). A Differential Semantics of Lazy Propagation, Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence, pages 364-371.
  • Madsen, A. L. and Jensen, F. (2005), Solving linear-quadratic conditional Gaussian influence diagrams, International Journal of Approximate Reasoning, 38 (3), pages: 263-282.
  • Madsen, A. L., Jensen, F., Kjærulff, U. B., Lang, M. (2005). HUGIN - The Tool for Bayesian Networks and Influence Diagrams, International Journal of Artificial Intelligence Tools 14 (3), pages 507-543.

2004

  • Bromley, J. and Jackson, N. A. and Clymer, O.J., and Giacomello, A.M. and Jensen, F.V., (2004), The use of Hugin to develop Bayesian networks as an aid to integrated water resource planning. Environmental Modelling and Software 20, pp 231-242.
  • Madsen, A. L. (2004), An Empirical Evaluation of Possible Variations of Lazy Propagation, Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence, pages 366-373.
  • Kjærulff, U. B. and Madsen, A. L. (2004), A Methodology for Acquiring Qualitative Knowledge for Probabilistic Graphical Models, Proceedings of the International Conference on Informational Processing and Management of Uncertainty in knowledge-based Systems, pages 143-150.
  • Kalwa, J. and Madsen, A. L. (2004), Sonar Image Quality Assessment for an Autonomous Underwater Vehicle, Proceedings of the 10th International Symposium on Robotics and Applications.
  • Madsen A. L., Kjærulff, U.B., Kalwa, J., Perrier, M. and Sotelo, M. A. (2004), Applications of Probabilistic Graphical Models to Diagnosis and Control of Autonomous Vehicles, Proceedings of the second Bayesian Application Modeling Workshop.

2003

  • Gebhardt, J., Detmer, H., and Madsen A. L., (2003), Prediction Parts Demand in the Automotive Industry --- An Application of Probabilistic Graphical Models, Proceedings of the first Bayesian Application Modeling Workshop.
  • Weidl, G., Madsen, A. L., and Dahlquist, E., (2003), Object Oriented Bayesian Networks for Industrial Process Operation, Proceedings of the first Bayesian Application Modeling Workshop.
  • Sotelo, M. A., Bergasa, L. M., Flores, R., Ocana, M., Doussin, M-H., Magdalena, L., Kalwa, J., Madsen, A. L., Perrier, M., Roland, D. and Corigliano, P., (2003), ADVanced On-Board Diagnosis and Control of Autonomous Systems II, Computer Aided Systems Theory --- EUROCAST 2003, Springer Verlag Lecture Notes on Computer Science, 2809, pages: 302-313.
  • Weidl, G., Madsen, A. L., and Dahlquist, E., (2003), Applications of Object Oriented Bayesian Networks for Causal Analysis of Process Disturbances, Proceedings ofthe 44th Scandinavian Conference on Simulation and Modeling.
  • Madsen, A. L., Lang, M., Kjærulff, U., and Jensen, F., (2003), The Hugin Tool for Learning Bayesian Networks, Proceedings of The Seventh European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, pages 549-605.
  • Madsen, A. L. and Jensen, F., (2003), Mixed Influence Diagrams, Proceedings of The Seventh European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, pages 208-219.

2002

  • Madsen, A. L., Olesen, K. G., and Dittmer, S. L., (2002), Practical Modeling of Bayesian Decision Problems - Exploiting Deterministic Relations, IEEE Transactions on Systems, Man. and Cybernetics Part B, 32(1) pages: 32-38.
  • Olesen, K. G. and Madsen, A. L., (2002), Maximal Prime Subgraph Decomposition of Bayesian Networks, IEEE Transactions on Systems, Man. and Cybernetics Part B, 32(1) pages: 21-31.
  • Jensen, F., Kjærulff, U., Lang, M., and Madsen, A. L., (2002), HUGIN - The Tool for Bayesian Networks and Influence Diagrams, Proceedings of the First European Workshop on Probabilistic Graphical Models, pages 212-221.
  • Weidl, G. and Madsen, A. L. and Dahlquist, E., (2002), Condition Monitoring, Root Cause Analysis and Decision Support on Urgency of Actions, Book Series FAIA (Frontiers in Artificial Intelligence and Applications), Soft Computing Systems - Design, Management and Applications, vol 87, pages 221-230.

2001

  • Lauritzen, S. L. and Jensen, F. (2001), Stable local computation with conditional Gaussian distributions. Statistics and Computing, 11(2):191 - 203.
  • Lauritzen, S. L. and Nilsson, D., (2001), Representing and solving decision problems with limited information. Management Science, 47, 1238 - 1251.
  • Madsen, A. L. and Nilsson, D. (2001), Solving Influence Diagrams using Shafer-Shenoy, HUGIN and Lazy Propagation, Proceedings of the 17th Conference on Uncertainty in Artificial Intelligence, pages 337-345.
  • Olesen, K. G. and Madsen, A. L., (2001), Maximal Prime Subgraph Decomposition of Bayesian Networks, Proceedings of The Thirteenth International Florida Artificial Intelligence Research Symposium Conference, pages 596-601.
  • Madsen, A. L. and Olesen, K. G. and Dittmer, S. L., (2001), Practical Modeling of Bayesian Decision Problems - Exploiting Deterministic Relations, Proceedings of The Thirteenth International Florida Artificial Intelligence Research Symposium Conference, pages 585-590.

2000

  • Nilsson, D. and Lauritzen, S. L., (2000) Evaluating Influence Diagrams using LIMIDs. In Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence, pp. 436-445.
  • Madsen, A. L., and D'Ambrosio, B., (2000), A Factorized Representation of Independence of Causal Influence and Lazy Propagation, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 8(2): 151-165.

1999

  • Madsen, A. L. and Jensen F. V., (1999), Lazy Propagation: A Junction Tree Inference Algorithm based on Lazy Evaluation, Artificial Intelligence, 113 (1-2): 203-245.
  • Madsen, A. L. and Jensen F. V. (1999), Lazy Evaluation of Symmetric Bayesian Decision Problems, Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence, pages 382-390.
  • Madsen, A. L. and Jensen F. V., (1999), Parallelization of Inference in Bayesian Networks, Research Report R-99-5002, Department of Computer Science, Aalborg University.
  • Skaanning, C., Jensen, F. V., Kjærulff, U. and Madsen, A. L., (1999), Acquisition and Transformation of Likelihoods to Conditional Probabilities for Bayesian Networks. Proceedings of the 1999 AAAI Spring Symposium on AI in Equipment Maintenance Service and Support, pages 34-40.
  • Madsen, A. L., and D'Ambrosio, B., (1999), A Factorized Representation of Independence of Causal Influence and Lazy Propagation, Proceedings of The Twelfth International Florida Artificial Intelligence Research Symposium Conference, pages 444-448.
  • Madsen, A. L., and D'Ambrosio, B., (1999), Independence of Causal Influence and Lazy Propagation, Proceedings of The Fifth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, pages 293-304.
  • Madsen, A. L. and Jensen F. V., (1999), Parallelization of Inference in Bayesian Networks, Research Report R-99-5002, Department of Computer Science, Aalborg University.

1998

  • Madsen, A. L., (1998), Lazy Propagation and Independence of Causal Influence, Proceedings of the 13th biennial European Conference on Artificial Intelligence, pages 612-613.
  • Madsen, A. L., Nielsen L. M., and Jensen F. V., (1998), ProbSy - A System for the Calculation of Probabilities in the Card Game Bridge, Proceedings of The Eleventh International Florida Artificial Intelligence Research Symposium Conference, pages 435-439.
  • Madsen, A. L. and Jensen F. V. (1998), Lazy Propagation in Junction Trees, Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pages 362-369.

1997

  • Lauritzen, S. L. and Jensen, F. V., (1997), Local computation with valuations from a commutative semigroup. Annals of Mathematics and Artificial Intelligence 21, 51-69.

1996

1995

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

1994

  • Jensen, F., (1994), Implementation aspects of various propagation algorithms in Hugin. Research Report R-94-2014, Department of Mathematics and Computer Science, Aalborg University, Denmark.
  • Jensen, F., Jensen, F. V., Dittmer, S. L., (1994), From influence diagrams to junction trees, Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, pages 367-373.
  • Jensen, F. V., Jensen, F., (1994), Optimal junction trees, Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, pages 360-366.

1993

1992

  • Lauritzen, S. L., (1992), Propagation of probabilities, means, and variances in mixed graphical models, Journal of the American Statistical Association (Theory and Methods), 87(420):1098-1108.
  • Olesen, K. G. and Lauritzen, S. L. and Jensen, F. V., (1992), aHugin: A system creating adaptive causal probabilistic networks, In Proceedings of the Eighth Conference on Uncertainty in Artificial Intelligence, pages 223-229.

1991

  • Jensen, F. V., Chamberlain, B., Nordahl, T. , Jensen, F., (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.

1990

  • Spiegelhalter, D. J. and Lauritzen, S. L., (1990), Sequential updating of conditional probabilities on directed graphical structures. Networks 20, 579-605.
  • Jensen, F. V. and Olesen, K. G. and Andersen, S. K., (1990), An algebra of Bayesian belief universes for knowledge-based systems. Networks, 20(5):637-659, Special Issue on Influence Diagrams.
  • Jensen, F. V. and Lauritzen, S. L. and Olesen, K. G., (1990), Bayesian updating in causal probabilistic networks by local computations, Computational Statistics Quarterly, 4:269-282.
  • Jensen, F. and Andersen, S. K., (1990), Approximations in Bayesian belief universes for knowledge-based systems, Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence, pages 162-169.
  • Lauritzen, S. L. and Dawid, A. P. and Larsen, B. N. and Leimer, H.-G., (1990), Independence properties of directed Markov fields. Networks, 20(5):491-505, Special Issue on Influence Diagrams.
  • Kjærulff, U. B., (1990), Triangulation of graphs - algorithms giving small total state space. Research Report R-90-09, Department of Mathematics and Computer Science, Aalborg University, Denmark.

1989

  • Andersen, S. K., Olesen, K. G., Jensen, F. V. and Jensen, F. (1989), Hugin – a shell for building Bayesian belief universes for expert systems, Proceedings of the 11th International Joint Conference on Artificial Intelligence, pages 1080-1085.

1988

  • Lauritzen, S. L. & 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 (Methodological), 50(2):157-224.

1983

  • Wermuth, N. and Lauritzen, S. L., (1983), Graphical and recursive models for contingency tables: Biometrika 70, 537-552.