-
2
-
-
0003614273
-
-
Springer-Verlag. (2nd ed. MIT Press)
-
P. Spirtes, C. Glymour, and R. Scheines. Causation, Prediction, and Search. Springer-Verlag, 1993. (2nd ed. MIT Press 2000).
-
(1993)
Causation, Prediction, and Search
-
-
Spirtes, P.1
Glymour, C.2
Scheines, R.3
-
4
-
-
0012315692
-
A Bayesian approach to causal discovery
-
C. Glymour and G. F. Cooper, editors. MIT Press
-
D. Heckerman, C. Meek, and G. Cooper. A Bayesian approach to causal discovery. In C. Glymour and G. F. Cooper, editors, Computation, Causation, and Discovery, pages 141-166. MIT Press, 1999.
-
(1999)
Computation, Causation, and Discovery
, pp. 141-166
-
-
Heckerman, D.1
Meek, C.2
Cooper, G.3
-
5
-
-
0012720970
-
Automated discovery of linear feedback models
-
C. Glymour and G. F. Cooper, editors. MIT Press
-
T. Richardson and P. Spirtes. Automated discovery of linear feedback models. In C. Glymour and G. F. Cooper, editors, Computation, Causation, and Discovery, pages 253-304. MIT Press, 1999.
-
(1999)
Computation, Causation, and Discovery
, pp. 253-304
-
-
Richardson, T.1
Spirtes, P.2
-
6
-
-
33646379109
-
Learning the structure of linear latent variable models
-
R. Silva, R. Scheines, C. Glymour, and P. Spirtes. Learning the structure of linear latent variable models. Journal of Machine Learning Research, 7:191-246, 2006. (Pubitemid 43668126)
-
(2006)
Journal of Machine Learning Research
, vol.7
, pp. 191-246
-
-
Silva, R.1
Scheines, R.2
Glymour, C.3
Spirtes, P.4
-
7
-
-
33749326177
-
A linear non-gaussian acyclic model for causal discovery
-
S. Shimizu, P. O. Hoyer, A. Hyvärinen, and A. J. Kerminen. A linear non-Gaussian acyclic model for causal discovery. Journal of Machine Learning Research, 7:2003-2030, 2006. (Pubitemid 44497456)
-
(2006)
Journal of Machine Learning Research
, vol.7
, pp. 2003-2030
-
-
Shimizu, S.1
Hoyer, P.O.2
Hyvarinen, A.3
Kerminen, A.4
-
8
-
-
40649092250
-
Distinguishing between cause and effect via kernel-based complexity measures for conditional probability densities
-
X. Sun, D. Janzing, and B. Schölkopf. Distinguishing between cause and effect via kernel-based complexity measures for conditional probability densities. Neurocomputing, pages 1248-1256, 2008.
-
(2008)
Neurocomputing
, pp. 1248-1256
-
-
Sun, X.1
Janzing, D.2
Schölkopf, B.3
-
13
-
-
29144480967
-
Kernel methods for measuring independence
-
A. Gretton, R. Herbrich, A. Smola, O. Bousquet, and B. Schölkopf. Kernel methods for measuring independence. Journal of Machine Learning Research, 6:2075-2129, 2005. (Pubitemid 41798124)
-
(2005)
Journal of Machine Learning Research
, vol.6
, pp. 2075-2129
-
-
Gretton, A.1
Herbrich, R.2
Smola, A.3
Bousquet, O.4
Scholkopf, B.5
-
14
-
-
85162004251
-
-
GPML code. http://www.gaussianprocess.org/gpml/code.
-
-
-
-
16
-
-
0003466536
-
Spline models for observational data
-
SIAM, Philadelphia
-
G.Wahba. Spline Models for Observational Data. Series in Applied Math., Vol. 59, SIAM, Philadelphia, 1990.
-
(1990)
Series in Applied Math.
, vol.59
-
-
Wahba, G.1
-
17
-
-
0001741790
-
A look at some data on the Old Faithful Geyser
-
A. Azzalini and A. W. Bowman. A look at some data on the Old Faithful Geyser. Applied Statistics, 39(3):357-365, 1990.
-
(1990)
Applied Statistics
, vol.39
, Issue.3
, pp. 357-365
-
-
Azzalini, A.1
Bowman, A.W.2
|