Bayesian robot system identification with input and output noise
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[1] E. M. Lifshitz,et al. Electrodynamics of continuous media , 1961 .
[2] W. Massy. Principal Components Regression in Exploratory Statistical Research , 1965 .
[3] N. Draper,et al. Applied Regression Analysis. , 1967 .
[4] V. Strassen. Gaussian elimination is not optimal , 1969 .
[5] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[6] P. E. Castro. Compact Numerical Methods for Computers: Linear Algebra and Function Minimization , 1978 .
[7] J. Hajnal. Perspectives in Probability and Statistics. Papers in Honour of M. S. Bartlett , 1978 .
[8] David A. Belsley,et al. Regression Analysis and its Application: A Data-Oriented Approach.@@@Applied Linear Regression.@@@Regression Diagnostics: Identifying Influential Data and Sources of Collinearity , 1981 .
[9] Gene H. Golub,et al. Matrix computations , 1983 .
[10] R. Tibshirani,et al. Monographs on statistics and applied probability , 1990 .
[11] H. Keselman,et al. Backward, forward and stepwise automated subset selection algorithms: Frequency of obtaining authentic and noise variables , 1992 .
[12] Ricardo D. Fierro,et al. The Total Least Squares Problem: Computational Aspects and Analysis (S. Van Huffel and J. Vandewalle) , 1993, SIAM Review.
[13] R. Farebrother. THE CHOLESKY DECOMPOSITION OF P-PP' , 1994 .
[14] D. Signorini,et al. Neural networks , 1995, The Lancet.
[15] Bruno Siciliano,et al. Modeling and Control of Robot Manipulators , 1995 .
[16] David B. Dunson,et al. Bayesian Data Analysis , 2010 .
[17] Geoffrey E. Hinton,et al. Bayesian Learning for Neural Networks , 1995 .
[18] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[19] Gerd Hirzinger,et al. Robotics research : the seventh international symposium , 1996 .
[20] W. WampleryDept. The Calibration Index and the Role of Input Noise in Robot Calibration , 1996 .
[21] Christopher G. Atkeson,et al. Constructive Incremental Learning from Only Local Information , 1998, Neural Computation.
[22] Matthew J. Beal,et al. Graphical Models and Variational Methods , 2001 .
[23] M. Opper,et al. Advanced mean field methods: theory and practice , 2001 .
[24] José Carlos Príncipe,et al. Efficient total least squares method for system modeling using minor component analysis , 2002, Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing.
[25] J.C. Principe,et al. Fast error whitening algorithms for system identification and control , 2003, 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718).
[26] M. Peruggia. Total Least Squares and Errors-in-Variables Modeling: Analysis, Algorithms and Applications , 2003 .
[27] Andrew W. Moore,et al. Locally Weighted Learning , 1997, Artificial Intelligence Review.
[28] Stefan Schaal,et al. The Bayesian backfitting relevance vector machine , 2004, ICML.
[29] Christopher K. I. Williams. How to Pretend That Correlated Variables Are Independent by Using Difference Observations , 2005, Neural Computation.
[30] Stefan Schaal,et al. Incremental Online Learning in High Dimensions , 2005, Neural Computation.
[31] Stefan Schaal,et al. Predicting EMG Data from M1 Neurons with Variational Bayesian Least Squares , 2005, NIPS.
[32] Susan A. Murphy,et al. Monographs on statistics and applied probability , 1990 .