Publications
Books
Winner of the DeGroot Prize 2002, the only book prize in the field of statistics.
Jensen, F. V. (2001), Bayesian Networks and Decision Graphs, Springer.
Jensen, F. V. (1996), An Introduction to Bayesian Networks, Springer.
Lauritzen, S. L. (1996), Graphical models, Clarendon Press, Oxford.
Book Chapters
2008
Weidl, G., Madsen, A. L. and Dahlquist, E. (2008). Decision Support on 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
2018
A component framework as an enabler for industrial cyber physical systems
Luis Neto ; Anders L. Madsen ; Nicolaj Søndberg-Jeppesen ; Ricardo Silva ; João Reis ; Peter McIntyre ; Gil Gonçalves
Publication Year: 2018, Page(s):339 – 344. DOI.
2017
Darío Ramos-López, Andrés R. Masegosa, Ana M. Martínez, Antonio Salmerón, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen. “MAP inference in dynamic hybrid Bayesian Networks” (January 2017). Prog Artif Intell, vol 6, pp 133-144. Online.
Anders L. Madsen, Frank Jensen, Antonio Salmerón, Helge Langseth, Thomas D. Nielsen. “A parallel algorithm for Bayesian network structure learning from large data sets” (2017). Knowledge-Based Systems 117, pp 46-55. Online.
David N. Barton, Youssouf Cisse, Bocary Kaya, Ibrahima N’Diaye, Harouna Yossi, Abdoulaye Diarra, Souleymane Keita, Amadou Dembele, Daouda Maiga, Graciela M. Rusch, Anders L. Madsen. ” Diagnosing Agrosilvopastoral practices using Bayesian networks” (April 2017). Agroforestry Systems, volume 91, Issue 2, pp 325-334.
Scientific Articles
2016
Barton, D.N., Benjamin, T., Cerdán, C.R., DeClerck, F., Madsen, A.L, Rusch, G.M., Salazar, A.G., Sanchez, D., Villanueva, C., Assessing ecosystem services from multifunctional trees in pastures using Bayesian belief networks. Ecosystem Services (2016), pp. 165-174. Online.
David N. Barton, Youssouf Cisse, Bocary Kaya, Ibrahima N.Diaye, Harouna Yossi, Abdoulaye Diarra, Souleymane Keita, Amadou Dembele, Daouda Maiga, Graciela M. Rusch, Anders L. Madsen (2016) Diagnosing agrosilvopastoral practices using Bayesian networks. Agroforestry Systems, pp 1-10. Online.
C.J. Butz, J.S. Oliveira, and A.L. Madsen, Bayesian Network Inference using Marginal Trees, International Journal of Approximate Reasoning, Vol. 68, 127-152, 2016
A.L. Madsen, C.J. Butz, J.S. Oliveira, A. dos Santos, On Tree Structures used by Simple Propagation for Bayesian Networks Inference, Twenty-ninth Canadian Conference on Artificial Intelligence (AI), 2016, pages 207-212.
C.J. Butz, J.S. Oliveira, A. dos Santos, and A.L. Madsen, Bayesian Network Inference with Simple Propagation, Twenty-ninth International Florida Artificial Intelligence Research Society Conference (FLAIRS), 2016, pages 650-655.
Cabañas, R., Cano, A., Gómez-Olmedo, M., and Madsen, A.L., Improvements to Variable Elimination and Symbolic Probabilistic Inference for evaluating Influence Diagrams, International Journal of Approximate Reasoning, Vol. 70, March 2016, Pages 13–35.
Garcia, A.B., Madsen, A.L., Vigre, H., A decision support system for the control of Campylobacter in chickens at farm level using data from Denmark, Journal of Agricultural Science, 12 pages. DOI.
Scientific Articles
2015
Madsen, A. L., and Butz, C. J. (2015). Exploiting Semantics in Bayesian Network Inference Using Lazy Propagation. In Advances in Artificial Intelligence Volume 9091 of the series Lecture Notes in Computer Science pp 3-15.
Weidl, G., Madsen, A. L., Tereshchenko, V., Dietmar, K. and Breuer, G. (2015). Early Recognition of Maneuvers in Highway Traffic . In proceedings of ECSQARU on 15-17 July 2015 in Compiegne, France, pages 529-540.
Salmerón, A, Rumi, R., Langseth, H., Madsen, A. L., Nielsen, T. D. (2015). MPE Inference in Conditional Linear Gaussian Networks. In proceedings of ECSQARU on 15-17 July 2015 in Compiegne, France, pages 407-416.
Madsen, A. L. and Salmerón, A (2015). Analysis of massive data streams using R and AMIDST. In book of abstracts of useR!2015 on 30 June -3 July 2015 in Aalborg, Denmark, page 171.
Borchani, H., Fernandez, A. M. M., Masegosa, A, Langseth, H., Nielsen, T. D., Salmerón, A., Fernández, A., Madsen, A. L., Sáez, R. (2015). Modeling concept drift: A probabilistic graphical model based approach. In proceedings of The Fourteenth International Symposium on Intelligent Data Analysis, 22-24 October 2015 in Saint-Etienne, France, pages 72-83.
Salmerón, A., Ramos-López, D., Borchani, H., Fernandez, A. M. M., Masegosa, A., Fernández, A., Langseth, H., Madsen, A. L., Nielsen, T. D. (2015). Parallel importance sampling in conditional linear Gaussian networks. To appear. The XVI Conference of the Spanish Association for Artificial Intelligence (CAEPIA’15). VBN. Online access.
Madsen, A. L., Jensen, F., Salmerón, A., Langseth, H., Nielsen, T. D. (2015). Parallelization of the PC Algorithm (2015). To appear. The XVI Conference of the Spanish Association for Artificial Intelligence (CAEPIA’15). VBN. Online access.
Borchani, H., Martinez, A. M., Masegosa, A, Langseth, H., Nielsen, T. D., Salmerón, A., Fernández, A., Madsen, A. L., Sáez, R (2015). Dynamic Bayesian modeling for risk prediction in credit operations (2015). To appear. The 13th Scandinavian Conference on Artificial Intelligence, Halmstad, Sweden, November 5-6, 2015.
Masegosa, A, Martinez, A. M., Borchani, H., Ramos-Lopez, D., Nielsen, T. D., Langseth, H., Salmerón, Madsen, A. L. (2015). AMIDST: Analysis of MassIve Data STreams (2015). In proceedings of The 27th Benelux Conference on Artificial Intelligence, Hasselt, Belgium, November 5-6, 2015.
Mohamed S. Sayed, Niels Lohse, Nicolaj Søndberg–Jeppesen, Anders L. Madsen (2015). SelSus: Towards A Reference Architecture for Diagnostics and Predictive Maintenance Using Smart Manufacturing Devices. Proceedings of the 2015 IEEE International Conference on Industrial Informatics (INDIN) Cambridge, United Kingdom. 6 pages. ISBN: 978-1-4799-6648-6
Anders Madsen, Nicolaj Søndberg-Jeppesen, Niels Lohse, Mohamed Sayed. A Methodology for Developing Local Smart Diagnostic Models Using Expert Knowledge. Proceedings of the 2015 IEEE International Conference on Industrial Informatics (INDIN) Cambridge, United Kingdom. 6 pages. ISBN: 978-1-4799-6648-6
Scientific Articles
2014
Madsen, A. L., Jensen, F., Salmerón, A. Karlsen, M., Langseth, H. and Nielsen, T.D. (2014). A New Method for Vertical Parallelisation of TAN Learning Based on Balanced Incomplete Block Designs. In proceedings of the Seventh European Workshop on Probabilistic Graphical Models (PGM), September 17-19, Utrecht, The Netherlands, pages 302-317. DOI: 10.1007/978-3-319-11433-0_20.
Weidl, G., Madsen, A. L., Dietmar, K. and Breuer, G. (2014). Optimizing Bayesian Networks for Recognition of Driving Maneuvers to Meet the Automotive Requirements. In proceedings of 2014 IEEE Multi-Conference on Systems and Control on 8-10 October 2014 in Nice, France, pages 1626-1631. DOI: 10.1109/ISIC.2014.6967630.
Nielsen, T.D., Hovda, S., Fernández, A., Langseth, H., Madsen, A.L., Masegosa, A., Salmerón. A. (2014). Requirement Engineering for a Small Project with Pre-Specified Scope. NIK: Norsk Informatikkonferanse 2014 – Høgskolen i Østfold, November 17-19, Fredrikstad, Norge, 12 pages. ISSN: 1892-0721.
Cabañas, R., Madsen, A.L., Cano, A., Gómez-Olmedo, M. On SPI for Evaluating Influence Diagrams. IPMU (1) 2014: 506-516.
Cabañas, R. Cano, A., Gómez-Olmedo, M., Madsen, A.L.: On SPI-Lazy Evaluation of Influence Diagrams. Probabilistic Graphical Models 2014: 97-112
Butz, C. J., de S. Oliveira, J., Madsen, A. L.: Bayesian Network Inference Using Marginal Trees. Probabilistic Graphical Models 2014: 81-96
Madsen, A. L., Jensen, F., Karlsen, M., Søndberg-Jeppesen, N.: Bayesian Networks with Function Nodes. Probabilistic Graphical Models 2014: 286-301
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
Madsen, A. L., Karlsen, M., Barker, G. C., Garcia, A. B., Hoorfar, J., Jensen, F (2013). A Software Package for Web Deployment of Probabilistic Graphical Models. In Proceedings of the Twelfth Scandinavian Conference on Artificial Intelligence (SCAI), pp 175 – 184. DOI10.3233/978-1-61499-330-8-175
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.
Scientific Articles
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.
Scientific Articles
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.
Scientific Articles
2010
Madsen, A. L. (2010). Improvements to message computation in Lazy propagation, Int. J. Approx. Reason., Vol 51 (5), pp 499-514, doi:10.1016/j.ijar.2010.01.009.
Scientific Articles
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.
Scientific Articles
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.
Scientific Articles
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.
Scientific Articles
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). The HUGIN Tool for Probabilistic Graphical Models, International Journal of Artificial Intelligence Tools 14 (3), pages 507-543.
Scientific Articles
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.
Scientific Articles
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. 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.
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.
Scientific Articles
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.
Scientific Articles
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.
Scientific Articles
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. DOI: 10.1142/S0218488500000113
Neil, M, Fenton, N., and Nielsen, L., (2000), Building large-scale Bayesian networks, The Knolwedge Engineering Review, 15 (3)
Scientific Articles
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.
Scientific Articles
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.
Scientific Articles
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.
Scientific Articles
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.
Scientific Articles
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.
Scientific Articles
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.
Scientific Articles
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.
Scientific Articles
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.
Scientific Articles
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.
Scientific Articles
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.
Scientific Articles
1983
Wermuth, N. and Lauritzen, S. L., (1983), Graphical and recursive models for contingency tables: Biometrika 70, 537-552.