Bayesian Control of Dynamic Systems

Bayesian networks for the static as well as for the dynamic case have gained an enormous interest in the research community of machine learning and pattern recognition. Although the parallels between dynamic Bayesian networks and description of dynamic systems by Kalman filters and difference equations are well known since many years, Bayesian networks have not been applied to problems in the area of adaptive control of dynamic systems.

[1]  Kevin P. Murphy,et al.  Learning the Structure of Dynamic Probabilistic Networks , 1998, UAI.

[2]  Kevin Murphy,et al.  Bayes net toolbox for Matlab , 1999 .

[3]  Nir Friedman,et al.  Learning Belief Networks in the Presence of Missing Values and Hidden Variables , 1997, ICML.

[4]  David Heckerman,et al.  Learning Gaussian Networks , 1994, UAI.

[5]  Gregory F. Cooper,et al.  Learning Hybrid Bayesian Networks from Data , 1999, Learning in Graphical Models.

[6]  David J. Spiegelhalter,et al.  Local computations with probabilities on graphical structures and their application to expert systems , 1990 .

[7]  Robert Cowell,et al.  Advanced Inference in Bayesian Networks , 1999, Learning in Graphical Models.

[8]  Heinrich Niemann,et al.  Non-linear Modelling of a Production Process by Hybrid Bayesian Networks , 2000, ECAI.

[9]  Uffe Kjærulff,et al.  A Computational Scheme for Reasoning in Dynamic Probabilistic Networks , 1992, UAI.

[10]  Michael I. Jordan.  Learning in Graphical Models , 1999, NATO ASI Series.

[11]  F. Fairman.  Introduction to dynamic systems: Theory, models and applications , 1979, Proceedings of the IEEE.

[12]  S. Lauritzen.  Propagation of Probabilities, Means, and Variances in Mixed Graphical Association Models , 1992 .

[13]  Xavier Boyen,et al.  Approximate Learning of Dynamic Models , 1998, NIPS.

[14]  Lin Liu,et al.  Estimating the parameters of mixed Bayesian networks from incomplete data , 1999, 1999 Information, Decision and Control. Data and Information Fusion Symposium, Signal Processing and Communications Symposium and Decision and Control Symposium. Proceedings (Cat. No.99EX251).

[15]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[16]  Steffen L. Lauritzen,et al.  Stable local computation with conditional Gaussian distributions , 2001, Stat. Comput..

[17]  Uri Lerner,et al.  Inference in Hybrid Networks: Theoretical Limits and Practical Algorithms , 2001, UAI.

[18]  Kristian G. Olesen,et al.  Causal Probabilistic Networks with Both Discrete and Continuous Variables , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Finn Verner Jensen,et al.  Introduction to Bayesian Networks , 2008, Innovations in Bayesian Networks.

[20]  Arthur Gelb,et al.  Applied Optimal Estimation , 1974 .

[21]  Daphne Koller,et al.  Sampling in Factored Dynamic Systems , 2001, Sequential Monte Carlo Methods in Practice.

[22]  Nir Friedman,et al.  The Bayesian Structural EM Algorithm , 1998, UAI.

[23]  Clayton Smith,et al.  Bayesian Control for Concentrating Mixed Nuclear Waste , 1999, UAI.

[24]  Frank Jensen,et al.  Optimal junction Trees , 1994, UAI.

[25]  Vladimír Kučera,et al.  Analysis and design of discrete linear control systems , 1991 .

[26]  Kevin P. Murphy,et al.  Inference and Learning in Hybrid Bayesian Networks , 1998 .

[27]  David J. Spiegelhalter,et al.  Probabilistic Networks and Expert Systems , 1999, Information Science and Statistics.

[28]  Robert Cowell,et al.  Introduction to Inference for Bayesian Networks , 1998, Learning in Graphical Models.

[29]  Uffe Kjærulff,et al.  dHugin: a computational system for dynamic time-sliced Bayesian networks , 1995 .