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  • 2023

    A Comparison of Different Marginalization Operations in Simple Propagation

    Madsen, A. L. & Butz, C., 2023, Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 17th European Conference, ECSQARU 2023, Proceedings. Bouraoui, Z. & Vesic, S. (eds.). Springer, Vol. 14294. p. 172-182 11 p. (Lecture Notes in Computer Science).

    Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

  • Dynamic Influence Diagram-Based Deep Reinforcement Learning Framework and Application for Decision Support for Operators in Control Rooms

    Mietkiewicz, J., Abbas, A. N., Amazu, C. W., Madsen, A. L. & Baldissone, G., 2023, Proceedings of the 33rd European Safety and Reliability Conference (ESREL 2023). Brito, M. P., Aven, T., Baraldi, P., Čepin, M. & Zio, E. (eds.). Research Publishing, p. 2806-2813 P531

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  • Enhancing Control Room Operator Decision Making: An Application of Dynamic Influence Diagrams in Formaldehyde Manufacturing

    Mietkiewicz, J. & Madsen, A. L., 2023, Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 17th European Conference, ECSQARU 2023, Arras, France, September 19–22, 2023, Proceedings. Bouraoui, Z. & Vesic, S. (eds.). Springer, p. 15-26 12 p. (Lecture Notes in Computer Science, Vol. LNCS 14294).

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  • Learning Style Classification by Using Bayesian Networks Based on the Index of Learning Style

    Usme, Y. P. V., Normann, M., Sapsai, I., Abke, J., Madsen, A. L. & Weidl, G., 19 Jun 2023, Proceedings of the 5th European Conference on Software Engineering Education, ECSEE 2023. Association for Computing Machinery, p. 73-82 10 p.

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    1 Citation (Scopus)
  • 2022

    A Decision Support System to Predict Acute Fish Toxicity

    Madsen, A. L., Moe, S. J., Braunbeck, T., Connors, K. A., Embry, M., Schirmer, K., Scholz, S., Wolf, R. & Lillicrap, A. D., 2022, Proceedings of the 11th International Conference on Probabilistic Graphical Models. Salmerón, A. & Rumi, R. (eds.). PMLR Press, p. 253-264 12 p. (The Proceedings of Machine Learning Research, Vol. 186).

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  • Data driven Bayesian network to predict critical alarm

    Mietkiewicz, J. & Madsen, A. L., 2022, Proceedings of the 32nd European Safety and Reliability Conference (ESREL 2022). Leva, M. C., Patelli, E., Podofillini, L. & Wilson, S. (eds.). Research Publishing, Singapore, 522

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  • Improvement of the Fine tuning algorithm

    Mietkiewicz, J. & Madsen, A. L., 2022, BNAIC/BeNeLearn - Joint International Scientific Conferences on AI and Machine Learning. 12 p.

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  • Online Updating of Conditional Linear Gaussian Bayesian Networks

    Madsen, A. L., Olesen, K. G., Jensen, F., Henriksen, P. A., Larsen, T. M. & Møller, J. M., 2022, Proceedings of the 11th International Conference on Probabilistic Graphical Models: PMLR. Salmeròn, A. & Rumi, R. (eds.). PMLR Press, Vol. 186. p. 97-108 12 p. (The Proceedings of Machine Learning Research, Vol. 186).

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  • 2020

    A Software System for Predicting Patient Flow at the Emergency Department of Aalborg University Hospital

    Madsen, A. L., Olesen, K. G., Møller, J. M., Søndberg-Jeppesen, N., Jensen, F., Larsen, T. M., Henriksen, P., Lindblad, M. & Christensen, T. S., 2020, Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR. Jaeger, M. & Dyhre Nielsen, T. (eds.). ML Research Press, p. 617-620 (The Proceedings of Machine Learning Research, Vol. 138).

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    1 Citation (Scopus)
  • Prediction of High Risk of Deviations in Home Care Deliveries

    Madsen, A. L., Olesen, K. G., Løvschall, H. L., Søndberg-Jeppesen, N., Jensen, F., Lindblad, M., Mogensen, M. L. & Christensen, T. S., 2020, Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR. Jaeger, M. & Dyhre Nielsen, T. (eds.). ML Research Press, p. 281-292 12 p. (The Proceedings of Machine Learning Research, Vol. 138).

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  • 2019

    Exploiting Symmetry of Independence in d-Separation

    Butz, C. J., dos Santos, A. E., Oliveira, J. & Madsen, A. L., 2019, Advances in Artificial Intelligence - 32nd Canadian Conference on Artificial Intelligence, Canadian AI 2019, Proceedings. Meurs, M.-J. & Rudzicz, F. (eds.). Cham: Springer, p. 42-54 13 p. (Lecture Notes in Computer Science, Vol. 11489).

    Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

  • Solving Influence Diagrams with Simple Propagation

    Madsen, A. L., Butz, C. J., Oliveira, J. & dos Santos, A. E., 2019, Advances in Artificial Intelligence - 32nd Canadian Conference on Artificial Intelligence, Canadian AI 2019, Proceedings. Rudzicz, F. & Meurs, M.-J. (eds.). Cham: Springer, p. 68-79 12 p. (Lecture Notes in Computer Science, Vol. 11489).

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  • 2018

    A component framework as an enabler for industrial cyber physical systems

    Neto, L., Madsen, A. L., Søndberg-Jeppesen, N., Silva, R., Reis, J., McIntyre, P. & Gonçalves, G., 15 Jun 2018, Proceedings - 2018 IEEE Industrial Cyber-Physical Systems, ICPS 2018. IEEE, p. 339-344 6 p.

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    2 Citations (Scopus)
  • Simple Propagation with Arc-Reversal in Bayesian Networks

    Madsen, A. L., Butz, C. J., Oliveira, J. & dos Santos, A. E., 2018, Proceedings of Machine Learning Research. PMLR Press, Vol. 72. p. 260-271 (The Proceedings of Machine Learning Research).

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    Open Access
    1 Citation (Scopus)
  • 2017

    Applying object-oriented bayesian networks for smart diagnosis and health monitoring at both component and factory level

    Madsen, A. L., Søndberg-Jeppesen, N., Sayed, M. S., Peschl, M. & Lohse, N., 2017, Advances in Artificial Intelligence: From Theory to Practice: 30th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2017, Proceedings. Springer, Vol. Part II. p. 132-141 10 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 10351 LNCS).

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    3 Citations (Scopus)
  • Bayesian models of data streams with Hierarchical Power Priors

    Masegosa, A., Nielsen, T. D., Langseth, H., Ramos-López, D., Salmerón, A. & Madsen, A. L., 2017, Proceedings of the 34th International Conference on Machine Learning. Vol. 70. p. 2334-2343

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    Open Access
    5 Citations (Scopus)
  • Financial Data Analysis with PGMs Using AMIDST

    Cabanas, R., Martinez, A. M., Masegosa, A. R., Ramos-Lopez, D., Sameron, A., Nielsen, T. D., Langseth, H. & Madsen, A. L., 30 Jan 2017, Proceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016. IEEE, p. 1284-1287 4 p. 7836816

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    4 Citations (Scopus)
  • Parameter learning algorithms for continuous model improvement using operational data

    Madsen, A. L., Jeppesen, N. S., Jensen, F., Sayed, M. S., Moser, U., Neto, L., Reis, J. & Lohse, N., 2017, Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 14th European Conference, ECSQARU 2017, Proceedings. Springer, p. 115-124 10 p. (Lecture Notes In Artificial Intelligence; No. 10369).

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  • 2016

    Bayesian Network Inference with Simple Propagation

    Butz, C. J., Oliveira, J., E. dos Santos, A. & Madsen, A. L., 2016, Proceedings of the Twenty-Ninth International Florida Artificial Intelligence Research Society Conference. AAAI Press Association for the Advancement of Artificial Intelligence, p. 650-655 6 p.

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    6 Citations (Scopus)
  • d-VMP: Distributed Variational Message Passing

    Masegosa, A., Martinez, A. M., Langseth, H., Nielsen, T. D., Salmerón, A., Ramos-López, D. & Madsen, A. L., 2016, JMLR Workshop and Conference Proceedings: Volume 52: Proceedings of the Eighth International Conference on Probabilistic Graphical Models. p. 321-332 12 p. (JMLR Workshop and Conference Proceedings, Vol. 52).

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    11 Citations (Scopus)
  • On Tree Structures Used by Simple Propagation

    Madsen, A. L., Butz, C. J., Oliveira, J. & E. dos Santos, A., 2016, Advances in Artificial Intelligence. Springer, p. 207-212 (Lecture Notes in Computer Science, Vol. 9673).

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    3 Citations (Scopus)
  • Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designs

    Salmerón, A., Madsen, A. L., Jensen, F., Langseth, H., Nielsen, T. D., Ramos-López, D., Martinez, A. M. & Masegosa, A., 2016, ECAI 2016: 22nd European Conference on Artificial Intelligence. IOS Press, p. 743-750 8 p. (Frontiers in Artificial Intelligence and Applications, Vol. 285).

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    1 Citation (Scopus)
  • Scalable MAP inference in Bayesian networks based on a Map-Reduce approach

    Ramos-López, D., Salmerón, A., Rumí, R., Martinez, A. M., Nielsen, T. D., Masegosa, A., Langseth, H. & Madsen, A. L., 2016, JMLR Workshop and Conference Proceedings: Volume 52: Proceedings of the Eighth International Conference on Probabilistic Graphical Models. p. 415-425 12 p. (JMLR Workshop and Conference Proceedings, Vol. 52).

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  • Situation Awareness and Early Recognition of Traffic Maneuvers

    Weidl, G., Madsen, A. L., Tereshchenko, V., Zhang, W., Wang, S. & Dietmar, K., 2016, Proceedings of the 9th EUROSIM Congress on Modelling and Simulation. IEEE, p. 7-16 10 p.

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  • 2015

    A Methodology for Developing Local Smart Diagnostic Models Using Expert Knowledge

    Madsen, A. L., Søndberg-Jeppesen, N., Lohse, N. & Sayed, M., 2015, Proceedings of the 2015 IEEE 13th International Conference on Industrial Informatics. IEEE, p. 1682 - 1687 6 p.

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    4 Citations (Scopus)
  • AMIDST: Analysis of MassIve Data STreams

    Masegosa, A., Martinez, A. M., Borchani, H., Ramos-López, D., Nielsen, T. D., Langseth, H., Salmerón, A. & Madsen, A. L., Oct 2015, The 27th Benelux Conference on Artificial Intelligence (BNAIC 2015).

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  • Dynamic Bayesian modeling for risk prediction in credit operations

    Borchani, H., Martinez, A. M., Masegosa, A., Langseth, H., Nielsen, T. D., Salmerón, A., Fernández, A., Madsen, A. L. & Sáez, R., 2015, The 13th Scandinavian Conference on Artificial Intelligence (SCAI'2015). IOS Press, p. 17-26 (Frontiers in Artificial Intelligence and Applications, Vol. 278).

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  • Early Recognition of Maneuvers in Highway Traffic

    Weidl, G., Madsen, A. L., Tereshchenko, V., Dietmar, K. & Breuel, G., 2015, Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 13th European Conference, ECSQARU 2015, Compiègne, France, July 15-17, 2015. Proceedings. Destercke, S. & Denoeux, T. (eds.). Springer, p. 529-540 (Lecture Notes in Computer Science; No. 9161).

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    4 Citations (Scopus)
  • Exploiting Semantics in Bayesian Network Inference Using Lazy Propagation

    Madsen, A. L. & Butz, C. J., 2015, Advances in Artificial Intelligence: 28th Canadian Conference on Artificial Intelligence, Canadian AI 2015, Halifax, Nova Scotia, Canada, June 2-5, 2015, Proceedings. Barbosa, D. & Milios, E. (eds.). Springer, p. 3-15

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    4 Citations (Scopus)
  • Modeling concept drift: A probabilistic graphical model based approach

    Borchani, H., Martinez, A. M., Masegosa, A. R., Langseth, H., Nielsen, T. D., Salmerón, A., Fernández, A., Madsen, A. L. & Sáez, R., 2015, Advances in Intelligent Data Analysis XIV: 14th International Symposium, IDA 2015, Saint Etienne. France, October 22 -24, 2015. Proceedings. Fromont, E., De Bie, T. & van Leeuwen, M. (eds.). Springer, p. 72-83 (Lecture Notes in Computer Science; No. 9385).

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  • MPE inference in conditional linear gaussian networks

    Salmerón, A., Rumí, R., Langseth, H., Madsen, A. L. & Nielsen, T. D., 2015, Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 13th European Conference, ECSQARU 2015, Compiègne, France, July 15-17, 2015. Proceedings. Destercke, S. & Denoeux, T. (eds.). Springer, p. 407-416 (Lecture Notes in Computer Science; No. 9161).

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  • Parallel importance sampling in conditional linear Gaussian networks

    Salmerón, A., Ramos-López, D., Borchani, H., Martinez, A. M., Masegosa, A. R., Fernández, A., Langseth, H., Madsen, A. L. & Nielsen, T. D., 2015, Advances in Artificial Intelligence: 16th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2015 Albacete, Spain, November 9–12, 2015 Proceedings. Puerta, J. M., Gámez, J. A., Dorronsoro, B., Barrenechea, E., Troncoso, A., Baruque, B. & Galar, M. (eds.). Springer, p. 36-46 (Lecture Notes in Computer Science; No. 9422).

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  • Parallelisation of the PC Algorithm

    Madsen, A. L., Jensen, F., Salmerón, A., Langseth, H. & Nielsen, T. D., 2015, Advances in Artificial Intelligence: 16th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2015 Albacete, Spain, November 9–12, 2015 Proceedings. Springer, p. 14-24 11 p. (Lecture Notes in Computer Science, Vol. 9422).

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    8 Citations (Scopus)
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  • SelSus: Towards A Reference Architecture for Diagnostics and Predictive Maintenance Using Smart Manufacturing Devices

    Sayed, M., Lohse, N., Søndberg-Jeppesen, N. & Madsen, A. L., 2015, Proceedings of the 2015 IEEE International Conference on Industrial Informatics. IEEE, p. 1700 - 1705 6 p. (IEEE International Conference on Industrial Informatics (INDIN)).

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    14 Citations (Scopus)
  • 2014

    A new method for vertical parallelisation of TAN learning based on balanced incomplete block designs

    Madsen, A. L., Jensen, F., Salmerón, A., Karlsen, M., Langseth, H. & Nielsen, T. D., 2014, Proceedings of the 7th European Workshop on Probabilistic Graphical Models. Springer Publishing Company, Vol. 8754. p. 302-317 16 p. (Lecture Notes in Computer Science).

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    11 Citations (Scopus)
  • Bayesian Network Inference Using Marginal Trees

    Butz, C. J., de S. Oliveira, J. & Madsen, A. L., 2014, Proceedings of the 7th European Workshop on Probabilistic Graphical Models. Springer Publishing Company, Vol. 8754. p. 81-96 (Lecture Notes in Computer Science).

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    2 Citations (Scopus)
  • Bayesian Networks with Function Nodes

    Madsen, A. L., 2014, Proceedings of the 7th European Workshop on Probabilistic Graphical Models. Springer Publishing Company, Vol. 8754. p. 286-301 (Lecture Notes in Computer Science).

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    4 Citations (Scopus)
  • On SPI for Evaluating Influence Diagrams

    Cabañas, R., Cano, A., Gómez-Olmedo, M. & Madsen, A. L., 2014, Proceedings of the 15th International IPMU Conference, Part I. Springer, Vol. 442. p. 506-516

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    3 Citations (Scopus)
  • On SPI-Lazy Evaluation of Influence Diagrams

    Cabañas, R., Cano, A., Gómez-Olmedo, M. & Madsen, A. L., 2014, Proceedings of the 7th European Workshop on Probabilistic Graphical Models. Springer Publishing Company, Vol. 8754. p. 97-112 (Lecture Notes in Computer Science).

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    3 Citations (Scopus)
  • Optimizing Bayesian Networks for Recognition of Driving Maneuvers to Meet the Automotive Requirements

    Weidl, G., Madsen, A. L., Dietmar, K. & Breuel, G., 2014, Intelligent Control (ISIC), 2014 IEEE International Symposium. IEEE, p. 1626 -1631 6 p.

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  • Requirement Engineering for a Small Project with Pre-Specified Scope

    Nielsen, T. D., Hovda, S., Antontio, F., Langseth, H., Madsen, A. L., Masegosa, A. & Salmerón, A., 2014, NIK: Norsk Informatikkonferanse . 12 p.

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  • 2013

    Approximate Lazy Evaluation of Influence Diagrams

    Cabañas, R., Cano, A., Gómez-Olmedo, M. & Madsen, A. L., 2013, Proceedings of the 15th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2013. Bielza, C., Salmerón, A., Alonso-Betanzos, A., Ignacio Hidalgo, J., Martínez, L., Troncoso, A., Corchado, E. & Corchado, J. M. (eds.). Springer Publishing Company, Vol. LNCP 8109. p. 321-331 (Lecture Notes in Computer Science, Vol. 8109).

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    1 Citation (Scopus)
  • A software package for web deployment of probabilistic graphical models

    Madsen, A. L., Karlsen, M., Barker, G. C., Garcia, A. B., Hoorfar, J., Jensen, F. & Vigre, H., 2013, Frontiers in Artificial Intelligence and Applications. Jaeger, M., Nielsen, T. D. & Viappiani, P. (eds.). IOS Press, Vol. 257. p. 175-184 10 p. (Frontiers in Artificial Intelligence and Applications, Vol. 257).

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    6 Citations (Scopus)
  • d-Separation: Strong Completeness of Semantics in Bayesian Network Inference

    Butz, C. J., Yan, W. & Madsen, A. L., 2013, Advances in Artificial Intelligence: 26th Canadian Conference on Artificial Intelligence, Canadian AI 2013, Regina, SK, Canada, May 28-31, 2013. Proceedings. Zaïane, O. R. & Zilles, S. (eds.). Springer Publishing Company, Vol. 7884. p. 13-24 (Lecture Notes in Computer Science, Vol. 7884).

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    4 Citations (Scopus)
  • Heuristics for determining the elimination ordering in the influence diagram evaluation with binary trees

    Cabañas, R., Cano, A., Gómez-Olmedo, M. & Madsen, A. L., 1 Dec 2013, Twelfth Scandinavian Conference on Artificial Intelligence. Jaeger, M., Nielsen, T. D. & Viappiani, P. (eds.). IOS Press, Vol. 257. p. 65-74 10 p. (Frontiers in Artificial Intelligence and Applications).

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    2 Citations (Scopus)
  • On Semantics of Inference in Bayesian Networks

    Butz, C. J., Yan, W. & Madsen, A. L., 2013, Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 12th European Conference, ECSQARU 2013, Utrecht, The Netherlands, July 8-10, 2013. Proceedings. van der Gaag, L. C. (ed.). Springer Publishing Company, p. 73-84 (Lecture Notes in Computer Science, Vol. 7958).

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    3 Citations (Scopus)
  • On the Tree Structure used by Lazy Propagation for Inference in Bayesian Networks

    Madsen, A. L. & Butz, C., 2013, Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 12th European Conference, ECSQARU 2013, Utrecht, The Netherlands, July 8-10, 2013. Proceedings. Springer Publishing Company, Vol. 7958. p. 400-411 (Lecture Notes in Computer Science, Vol. 7958).

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    1 Citation (Scopus)
  • 2012

    On the Importance of Elimination Heuristics in Lazy Propagation

    Madsen, A. L. & Butz, C. J., 2012, Proceedings of the 6th European Workshop on Probabilistic Graphical Models. Universidad de Granada, p. 227-234 8 p.

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    6 Citations (Scopus)