An Ising Model for Road Traffic Inference
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[1] T. Plefka. Convergence condition of the TAP equation for the infinite-ranged Ising spin glass model , 1982 .
[2] J J Hopfield,et al. Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.
[3] D. Amit,et al. Statistical mechanics of neural networks near saturation , 1987 .
[4] M. Mézard,et al. Spin Glass Theory and Beyond , 1987 .
[5] E. T. Jaynes,et al. Probability Theory as Logic , 1990 .
[6] J. Yedidia,et al. How to expand around mean-field theory using high-temperature expansions , 1991 .
[7] A. Hasman,et al. Probabilistic reasoning in intelligent systems: Networks of plausible inference , 1991 .
[8] 大西 仁,et al. Pearl, J. (1988, second printing 1991). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan-Kaufmann. , 1994 .
[9] Yoshiyuki Kabashima,et al. Belief propagation vs. TAP for decoding corrupted messages , 1998 .
[10] Hilbert J. Kappen,et al. Efficient Learning in Boltzmann Machines Using Linear Response Theory , 1998, Neural Computation.
[11] W. Freeman,et al. Generalized Belief Propagation , 2000, NIPS.
[12] Brendan J. Frey,et al. Factor graphs and the sum-product algorithm , 2001, IEEE Trans. Inf. Theory.
[13] Tom Minka,et al. Expectation Propagation for approximate Bayesian inference , 2001, UAI.
[14] William T. Freeman,et al. Correctness of Belief Propagation in Gaussian Graphical Models of Arbitrary Topology , 1999, Neural Computation.
[15] Nikolaus Hansen,et al. Completely Derandomized Self-Adaptation in Evolution Strategies , 2001, Evolutionary Computation.
[16] Martin J. Wainwright,et al. Stochastic processes on graphs with cycles: geometric and variational approaches , 2002 .
[17] M. Mézard,et al. Random K-satisfiability problem: from an analytic solution to an efficient algorithm. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.
[18] Fabrice Marchal,et al. Real Cases Applications of the Fully Dynamic METROPOLIS Tool-Box: An Advocacy for Large-Scale Mesoscopic Transportation Systems , 2002 .
[19] Yee Whye Teh,et al. Approximate inference in Boltzmann machines , 2003, Artif. Intell..
[20] E. Jaynes. Probability theory : the logic of science , 2003 .
[21] Hilbert J. Kappen,et al. On the properties of the Bethe approximation and loopy belief propagation on binary networks , 2004 .
[22] Tom Heskes,et al. On the Uniqueness of Loopy Belief Propagation Fixed Points , 2004, Neural Computation.
[23] Delbert Dueck,et al. Clustering by Passing Messages Between Data Points , 2007, Science.
[24] Arnaud de La Fortelle,et al. A Belief Propagation Approach to Traffic Prediction using Probe Vehicles , 2007, 2007 IEEE Intelligent Transportation Systems Conference.
[25] Kazuyuki Tanaka,et al. Approximate Learning Algorithm in Boltzmann Machines , 2009, Neural Computation.
[26] Kenji Fukumizu,et al. Graph Zeta Function in the Bethe Free Energy and Loopy Belief Propagation , 2009, NIPS.
[27] Thierry Mora,et al. Constraint satisfaction problems and neural networks: A statistical physics perspective , 2008, Journal of Physiology-Paris.
[28] Anne Auger,et al. Learning Multiple Belief Propagation Fixed Points for Real Time Inference , 2009, Physica A: Statistical Mechanics and its Applications.
[29] Fabien Moutarde,et al. Spatial and temporal analysis of traffic states on large scale networks , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.
[30] Fabien Moutarde,et al. Analysis of network-level traffic states using locality preservative non-negative matrix factorization , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).
[31] R. Monasson,et al. Adaptive Cluster Expansion for the Inverse Ising Problem: Convergence, Algorithm and Tests , 2011, 1110.5416.
[32] R. Monasson,et al. High-dimensional inference with the generalized Hopfield model: principal component analysis and corrections. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.
[33] J. Berg,et al. Bethe–Peierls approximation and the inverse Ising problem , 2011, 1112.3501.
[34] V. Martín. Modélisation probabiliste et inférence par l'algorithme Belief Propagation , 2013 .