Wim Wiegerinck - researchr alias

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Viewing Publication 1 - 31 from 31

2016

Bonaparte: Bayesian Networks to Give Victims back their NamesBert Kappen, Wim Wiegerinck. ercim, 2016(107), 2016. [doi]

2013

Bayesian Networks, Introduction and Practical ApplicationsWim Wiegerinck, Willem Burgers, Bert Kappen. In Monica Bianchini, Marco Maggini, Lakhmi C. Jain, editors, Handbook on Neural Information Processing. Volume 49 of Intelligent Systems Reference Library, pages 401-431, Springer, 2013. [doi]

2011

Stochastic optimal control of state constrained systemsBart van den Broek, Wim Wiegerinck, Bert Kappen. ijcon, 84(3):597-615, 2011. [doi]

2010

A Bayesian petrophysical decision support system for estimation of reservoir compositionsWillem Burgers, Wim Wiegerinck, Bert Kappen, Mirano Spalburg. eswa, 37(12):7526-7532, 2010. [doi]

Risk Sensitive Path Integral ControlBart van den Broek, Wim Wiegerinck, Hilbert J. Kappen. uai 2010: 615-622 [doi]

Bayesian Networks for Expert Systems: Theory and Practical ApplicationsWim Wiegerinck, Bert Kappen, Willem Burgers. In Robert Babuska, Frans C. A. Groen, editors, Interactive Collaborative Information Systems. Volume 281 of Studies in Computational Intelligence, pages 547-578, Springer, 2010. [doi]

2008

Graphical Model Inference in Optimal Control of Stochastic Multi-Agent SystemsBart van den Broek, Wim Wiegerinck, Bert Kappen. jair, 32:95-122, 2008. [doi]

2007

2006

Stochastic Optimal Control in Continuous Space-Time Multi-Agent SystemsWim Wiegerinck, Bart van den Broek, Bert Kappen. uai 2006: [doi]

2005

Modeling Bayesian Networks by Learning from ExpertsWim Wiegerinck. bnaic 2005: 305-312

Approximations with Reweighted Generalized Belief PropagationWim Wiegerinck. aistats 2005: [doi]

2004

Improving classification with latent variable models by sequential constraint optimizationMachiel Westerdijk, Wim Wiegerinck. ijon, 56:167-185, 2004. [doi]

2003

Approximate Expectation MaximizationTom Heskes, Onno Zoeter, Wim Wiegerinck. nips 2004: 353-360 [doi]

Optimising newspaper sales using neural-Bayesian technologyTom Heskes, Jan-Joost Spanjers, Bart Bakker, Wim Wiegerinck. nca, 12(3-4):212-219, 2003. [doi]

Clinical Applications of Artificial Neural Networks: Richard Dybowski, Vanya Gant (Eds.), Cambridge University Press, Cambridge, UK, New York, Oakleigh, Madrid, Cape Town, 2001, 378 pp., 55 figures, 25 tables, Hardcover, ISBN 0521662710Wim Wiegerinck. artmed, 27(2):223-226, 2003.

2002

IPF for Discrete Chain Factor GraphsWim Wiegerinck, Tom Heskes. uai 2002: 560-567

Fractional Belief PropagationWim Wiegerinck, Tom Heskes. nips 2003: 438-445 [doi]

2001

Novel iteration schemes for the Cluster Variation MethodHilbert J. Kappen, Wim Wiegerinck. nips 2001: 415-422 [doi]

Deterministic Generative Models for Fast Feature DiscoveryMachiel Westerdijk, David Barber, Wim Wiegerinck. datamine, 5(4):337-363, 2001.

2000

1999

Approximate inference for medical diagnosisWim Wiegerinck, Hilbert J. Kappen, E. W. M. T. ter Braak, W. J. P. P. ter Burg, Marcel J. Nijman, Y. L. O, Jan P. Neijt. prl, 20(11-13):1231-1239, 1999. [doi]

1998

Tractable Variational Structures for Approximating Graphical ModelsDavid Barber, Wim Wiegerinck. nips 1999: 183-189 [doi]

1996

A theoretical comparison of batch-mode, on-line, cyclic, and almost-cyclic learningTom Heskes, Wim Wiegerinck. tnn, 7(4):919-925, 1996. [doi]

How Dependencies between Successive Examples Affect On-Line LearningWim Wiegerinck, Tom Heskes. neco, 8(8):1743-1765, 1996. [doi]

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