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Frontiers of Electrical and Electronic Engineering

ISSN 2095-2732

ISSN 2095-2740(Online)

CN 10-1028/TM

Front. Electr. Electron. Eng.  2010, Vol. 5 Issue (3): 237-240   https://doi.org/10.1007/s11460-010-0100-4
  Research articles 本期目录
Emerging themes on information theory and Bayesian approach
Emerging themes on information theory and Bayesian approach
Lei XU1,Yanda LI2,
1.Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China; 2.Department of Automation, Tsinghua University, Beijing 100084, China;
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出版日期: 2010-09-05
 引用本文:   
. Emerging themes on information theory and Bayesian approach[J]. Front. Electr. Electron. Eng., 2010, 5(3): 237-240.
Lei XU, Yanda LI, . Emerging themes on information theory and Bayesian approach. Front. Electr. Electron. Eng., 2010, 5(3): 237-240.
 链接本文:  
https://academic.hep.com.cn/fee/CN/10.1007/s11460-010-0100-4
https://academic.hep.com.cn/fee/CN/Y2010/V5/I3/237
Shannon C E. A mathematical theory of communication. Bell System Technical Journal, 1948, 27: 379―423, 623―656
Rao C R. Information and accuracy attainable in the estimation of statisticalparameters. Bulletin of the Calcutta MathematicalSociety, 1945, 37: 81―91
Kullback S, Leibler R A. On information and sufficiency. Annals of Mathematical Statistics, 1951, 22 (1): 79―86

doi: 10.1214/aoms/1177729694
Jaynes E T. Information theory and statistical mechanics. Physical Review, 1957, 106(4): 620―630

doi: 10.1103/PhysRev.106.620
Shore J, Johnson R. Properties of cross-entropyminimization. IEEE Transactions on InformationTheory, 1981, 27(4): 472―482

doi: 10.1109/TIT.1981.1056373
Chentsov N N. Statistical Decision Rules and Optimal Inference, Translations ofMathematical Monographs; v. 53. AmericanMathematical Society, 1982
Amari S. Differential-GeometricalMethods in Statistics. Lecture Notes inStatistics, Berlin: Springer-Verlag, 1985
Dempster A P, Laird N M, Rubin D B. Maximum-likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. SeriesB, 1977, 39(1): 1―38
Yuille A L, Kersten D. Vision as Bayesian inference:Analysis by synthesis? Trends in CognitiveSciences, 2006, 10(7): 301―308

doi: 10.1016/j.tics.2006.05.002
Xu L. YING-YANGmachines: A Bayesian-Kullback scheme for unified learning and newresults on vector quantization. In: Proceedingsof the International Conference on Neural Information Processing (ICONIP95). 1995, 977―988
Hinton G E, Dayan P, Frey B J, Neal R N. The wake-sleepalgorithm for unsupervised learning neural networks. Science, 1995, 268(5214): 1158―1160

doi: 10.1126/science.7761831
Xu L. TemporalBYY learning for state space approach, hidden Markov model and blindsource separation. IEEE Transactions onSignal Processing, 2000, 48(7): 2132―2144

doi: 10.1109/78.847796
Akaike H. Anew look at the statistical model identification. IEEE Transactions on Automatic Control, 1974, 19(6): 716―723

doi: 10.1109/TAC.1974.1100705
Solomonoff R J. A formal theory of inductive inference. Part I. Information and Control, 1964, 7(1): 1―22

doi: 10.1016/S0019-9958(64)90223-2
Kolmogorov A N. Three approaches to the quantitative definition of information. Problems of Information Transmission, 1965, 1(1): 1―11
Wallace C S, Boulton D M. An information measure forclassification. Computer Journal, 1968, 11(2): 185―194
Schwarz G. Estimatingthe dimension of a model. Annals of Statistics, 1978, 6(2): 461―464

doi: 10.1214/aos/1176344136
Rissanen J. Modelingby shortest data description. Automatica, 1978, 14: 465―471

doi: 10.1016/0005-1098(78)90005-5
MacKay D J C. Bayesian interpolation. Neural Computation, 1992, 4(3): 415―447

doi: 10.1162/neco.1992.4.3.415
McGrory C A, Titterington D M. Variational approximationsin Bayesian model selection for finite mixture distributions. Computational Statistics & Data Analysis, 2007, 51(11): 5352―5367

doi: 10.1016/j.csda.2006.07.020
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