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C. Furtlehner, Yufei Han, Jean-Marc Lasgouttes, Victorin Martin, F. Marchal, F. Moutarde (2010)
Spatial and temporal analysis of traffic states on large scale networks13th International IEEE Conference on Intelligent Transportation Systems
Danny Bickson (2009)
Gaussian belief propagation : theory and application (פעפוע אמונות גאוסייני.)
Yee Welling (2001)
Passing and Bouncing Messages for Generalised Inference
PUMAS project. http://team.inria.fr/pumas
A. Ihler, John III, A. Willsky (2005)
Loopy Belief Propagation: Convergence and Effects of Message ErrorsJ. Mach. Learn. Res., 6
S. Tatikonda, Michael Jordan (2002)
Loopy Belief Propogation and Gibbs MeasuresArXiv, abs/1301.0605
A. Dempster, N. Laird, D. Rubin (1977)
Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper
Brian Smith, Billy Williams, R. Oswald (2002)
Comparison of parametric and nonparametric models for traffic flow forecastingTransportation Research Part C-emerging Technologies, 10
M. Welling, Y. Teh (2003)
Approximate inference in Boltzmann machinesArtif. Intell., 143
T. Cover, P. Hart (1967)
Nearest neighbor pattern classificationIEEE Trans. Inf. Theory, 13
Xavier Boyen (2002)
Inference and learning in complex stochastic processes
Jerome Friedman, T. Hastie, R. Tibshirani (2008)
Sparse inverse covariance estimation with the graphical lasso.Biostatistics, 9 3
E Sudderth, A Ihler, M Isard, W Freeman, A Willsky (2010)
Nonparametric Belief PropagationCommun. ACM, 53
S. Cocco, R. Monasson (2011)
Adaptive Cluster Expansion for the Inverse Ising Problem: Convergence, Algorithm and TestsJournal of Statistical Physics, 147
M. Wainwright (2006)
Estimating the "Wrong" Graphical Model: Benefits in the Computation-Limited SettingJ. Mach. Learn. Res., 7
D. MacKay, J. Yedidia (2001)
A conversation about the Bethe free energy and sum-product
Pradeep Ravikumar, M. Wainwright, J. Lafferty (2010)
High-dimensional Ising model selection using ℓ1-regularized logistic regressionAnnals of Statistics, 38
K. Beyer, J. Goldstein, R. Ramakrishnan, U. Shaft (1999)
When Is ''Nearest Neighbor'' Meaningful?
C. Furtlehner, Jean-Marc Lasgouttes, A. Fortelle (2007)
A Belief Propagation Approach to Traffic Prediction using Probe Vehicles2007 IEEE Intelligent Transportation Systems Conference
Danny Bickson, D. Dolev, O. Shental, P. Siegel, J. Wolf (2008)
Gaussian belief propagation based multiuser detection2008 IEEE International Symposium on Information Theory
P. Cochat, L. Vaucoret, J. Sarles (2008)
Et alEvidence Based Mental Health, 11
(2004)
On soft evidence in bayesian networks
ii) How to construct the dependencies between latent variables σ i in an efficient way in terms of prediction performance?
J. Pearl (1988)
Chapter 2 – BAYESIAN INFERENCE
T. Han, Huazhong Ning, Thomas Huang (2006)
Efficient Nonparametric Belief Propagation with Application to Articulated Body Tracking2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), 1
R. Baxter (1982)
Exactly solved models in statistical mechanics
Erik Sudderth, A. Ihler, W. Freeman, A. Willsky (2003)
Nonparametric belief propagation2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings., 1
A. Jalali, Christopher Johnson, Pradeep Ravikumar (2011)
On Learning Discrete Graphical Models using Greedy MethodsArXiv, abs/1107.3258
Wanli Min, L. Wynter (2011)
Real-time road traffic prediction with spatio-temporal correlationsTransportation Research Part C-emerging Technologies, 19
C. Furtlehner, Jean-Marc Lasgouttes, A. Auger (2009)
Learning Multiple Belief Propagation Fixed Points for Real Time InferenceArXiv, abs/0903.4860
and as an in
H. Chan, Adnan Darwiche (2003)
On the revision of probabilistic beliefs using uncertain evidenceArtif. Intell., 163
C. Furtlehner, Yufei Han, Jean-Marc Lasgouttes, Victorin Martin (2012)
Pairwise MRF Calibration by Perturbation of the Bethe Reference PointArXiv, abs/1210.5338
Victorin Martin, C. Furtlehner, Yufei Han, Jean-Marc Lasgouttes (2014)
GMRF Estimation under Topological and Spectral Constraints
C. Furtlehner, Jean-Marc Lasgouttes, Arnaud De, La Fortelle
A Belief Propagation Approach to Traffic Prediction using Probe Vehicles
J. Mooij, H. Kappen (2005)
Sufficient Conditions for Convergence of the Sum–Product AlgorithmIEEE Transactions on Information Theory, 53
E. Jaynes, W. Crow (1990)
Probability Theory as Logic
J. Yedidia, W. Freeman, Yair Weiss (2005)
Constructing free-energy approximations and generalized belief propagation algorithmsIEEE Transactions on Information Theory, 51
J. Herrera, D. Work, R. Herring, X. Ban, Quinn Jacobson, A. Bayen (2009)
Evaluation of traffic data obtained via GPS-enabled mobile phones: The Mobile Century field experimentTransportation Research Part C-emerging Technologies, 18
大西 仁 (1994)
Pearl, J. (1988, second printing 1991). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan-Kaufmann., 1
A. Doucet, Nando Freitas, N. Gordon (2001)
Sequential Monte Carlo Methods in Practice
A. Doucet, Nando Freitas, N. Gordon (2001)
An Introduction to Sequential Monte Carlo Methods
V. Martín (2013)
Modélisation probabiliste et inférence par l'algorithme Belief Propagation
F. Kschischang, B. Frey, Hans-Andrea Loeliger (2001)
Factor graphs and the sum-product algorithmIEEE Trans. Inf. Theory, 47
T. Koski, J. Noble (2009)
Factor Graphs and the Sum Product Algorithm
M. Mézard, G. Parisi, M. Virasoro (1986)
Spin Glass Theory And Beyond: An Introduction To The Replica Method And Its Applications
K. Gupta (2011)
Probability Theory: The Logic of Science
J. Darroch, D. Ratcliff (1972)
Generalized Iterative Scaling for Log-Linear ModelsAnnals of Mathematical Statistics, 43
C. Furtlehner (2013)
Approximate inverse Ising models close to a Bethe reference pointJournal of Statistical Mechanics: Theory and Experiment, 2013
J. Pearl (1991)
Probabilistic reasoning in intelligent systems - networks of plausible inference
Muneki Yasuda, Kazuyuki Tanaka (2009)
Approximate Learning Algorithm in Boltzmann MachinesNeural Computation, 21
E. Jaynes (1968)
Prior Probabilities
M. Mézard, Thierry Mora (2008)
Constraint satisfaction problems and neural networks: A statistical physics perspectiveJournal of Physiology-Paris, 103
Tony Han, Huazhong Ning, Thomas Huang (2006)
Efficient Nonparametric Belief Propagation with Application to Articulated Body Tracking
T. Minka (2001)
Expectation Propagation for approximate Bayesian inference
We present a novel method for online inference of real-valued quantities on a large network from very sparse measurements. The target application is a large scale system, like e.g. a traffic network, where a small varying subset of the variables is observed, and predictions about the other variables have to be continuously updated. A key feature of our approach is the modeling of dependencies between the original variables through a latent binary Markov random field. This greatly simplifies both the model selection and its subsequent use. We introduce the mirror belief propagation algorithm, that performs fast inference in such a setting. The offline model estimation relies only on pairwise historical data and its complexity is linear w.r.t. the dataset size. Our method makes no assumptions about the joint and marginal distributions of the variables but is primarily designed with multimodal joint distributions in mind. Numerical experiments demonstrate both the applicability and scalability of the method in practice.
Annals of Mathematics and Artificial Intelligence – Springer Journals
Published: Aug 5, 2015
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