-
1
-
-
0003535936
-
-
John Wiley & Sons, Hoboken, NJ, 2nd edition
-
A. Agresti. Categorical Data Analysis. John Wiley & Sons, Hoboken, NJ., 2nd edition, 2002.
-
(2002)
Categorical Data Analysis
-
-
Agresti, A.1
-
4
-
-
0002460150
-
The alarm monitoring system: A case study with two probabilistic inference techniques for belief networks
-
Springer-Verlag, Berlin
-
I. Beinlich, H. Suermondt, R. Chevaz, and G. Cooper. The alarm monitoring system: A case study with two probabilistic inference techniques for belief networks. In Proceedings of the 2nd European Conference on Artificail Intelligence in Medicine, pages 247-256. Springer-Verlag, Berlin, 1989.
-
(1989)
Proceedings of the 2nd European Conference on Artificail Intelligence in Medicine
, pp. 247-256
-
-
Beinlich, I.1
Suermondt, H.2
Chevaz, R.3
Cooper, G.4
-
5
-
-
33747856187
-
Protein calassification using probabilistic chain graphs and the gene ontology structure
-
S. Carroll and V. Pavlovic. Protein calassification using probabilistic chain graphs and the gene ontology structure. Bioinformatics, 22(15):1871-1878, 2006.
-
(2006)
Bioinformatics
, vol.22
, Issue.15
, pp. 1871-1878
-
-
Carroll, S.1
Pavlovic, V.2
-
6
-
-
0042496103
-
Learning equivalence classes of bayesian-network structures
-
D. M. Chickering. Learning equivalence classes of bayesian-network structures. J. Mach. Learn. Res., 2:445-498, 2002.
-
(2002)
J. Mach. Learn. Res
, vol.2
, pp. 445-498
-
-
Chickering, D.M.1
-
7
-
-
0003687180
-
-
Springer-Verlag, New York
-
R. G. Cowell, A. P. Dawid, S. L. Lauritzen, and D. J. Spiegelhalter. Probabilistic Networks and Expert Systems. Springer-Verlag, New York, 1999.
-
(1999)
Probabilistic Networks and Expert Systems
-
-
Cowell, R.G.1
Dawid, A.P.2
Lauritzen, S.L.3
Spiegelhalter, D.J.4
-
9
-
-
37249059395
-
A sinful approach to gaussian graphical model selection
-
M. Drton and M. Perlman. A sinful approach to gaussian graphical model selection. J. Stat. Plan. Infer., 138:1179-1200, 2008.
-
(2008)
J. Stat. Plan. Infer
, vol.138
, pp. 1179-1200
-
-
Drton, M.1
Perlman, M.2
-
12
-
-
45849134070
-
Sparse inverse covariance estimation with the graphical lasso
-
doi: doi:10.1093/biostatistics/kxm045
-
J. Friedman, T. Hastie, and R. Tibshirani. Sparse inverse covariance estimation with the graphical lasso. Biostatistics,2001. doi: doi:10.1093/biostatistics/kxm045.
-
(2001)
Biostatistics
-
-
Friedman, J.1
Hastie, T.2
Tibshirani, R.3
-
13
-
-
0037262841
-
Being bayesian about network structure: A bayesian approach to structure discovery in bayesian networks
-
N. Friedman and D. Koller. Being bayesian about network structure: a bayesian approach to structure discovery in bayesian networks. Mach. Learn., 50:95-126, 2003.
-
(2003)
Mach. Learn
, vol.50
, pp. 95-126
-
-
Friedman, N.1
Koller, D.2
-
14
-
-
0002219642
-
Learning bayesian network structure from massive datasets: The "sparse candidate" algorithm
-
Stockholm, Sweden
-
N. Friedman, I. Nachmana, and D. Pe'er. Learning bayesian network structure from massive datasets: The "sparse candidate" algorithm. In Proceedings of the Fifteenth Conference on Uncertainty in Artificail Intelligence, pages 206-215, Stockholm, Sweden, 1999.
-
(1999)
Proceedings of the Fifteenth Conference on Uncertainty in Artificail Intelligence
, pp. 206-215
-
-
Friedman, N.1
Nachmana, I.2
Pe'er, D.3
-
15
-
-
0001546180
-
The chain graph markov property
-
M. Frydenberg. The chain graph markov property. Scand. J. Statist., 17:333-353, 1990.
-
(1990)
Scand. J. Statist
, vol.17
, pp. 333-353
-
-
Frydenberg, M.1
-
16
-
-
33947524259
-
Estimating high-dimensional directed acyclic graphs with the pcalgorithm
-
M. Kalisch and P. Bühlmann. Estimating high-dimensional directed acyclic graphs with the pcalgorithm. J. Mach. Learn. Res., 8:616-636, 2007.
-
(2007)
J. Mach. Learn. Res
, vol.8
, pp. 616-636
-
-
Kalisch, M.1
Bühlmann, P.2
-
18
-
-
0036420729
-
Chain graph models and their causal interpretations (with discussion)
-
S. L. Lauritzen and T. S. Richardson. Chain graph models and their causal interpretations (with discussion). J. R. Statist. Soc. B, 64:321-361, 2002.
-
(2002)
J. R. Statist. Soc. B
, vol.64
, pp. 321-361
-
-
Lauritzen, S.L.1
Richardson, T.S.2
-
20
-
-
33747163541
-
High-dimensional graphs and variable selection with the lasso
-
N. Meinshausen and P. Bühlmann. High-dimensional graphs and variable selection with the lasso. Ann. Statist., 34:1436-1462, 2006.
-
(2006)
Ann. Statist
, vol.34
, pp. 1436-1462
-
-
Meinshausen, N.1
Bühlmann, P.2
-
22
-
-
58149288293
-
-
1 -regularized logistic regression. Technical Report 750, Department of Statistics, University of California, Berkeley., 2008. URL http://www.stat.berkeley.edu/tech-report/750. pdf.
-
1 -regularized logistic regression. Technical Report 750, Department of Statistics, University of California, Berkeley., 2008. URL http://www.stat.berkeley.edu/tech-report/750. pdf.
-
-
-
-
23
-
-
33645062435
-
On block ordering of variables in graphical modelling
-
A. Roverato and L. La Rocca. On block ordering of variables in graphical modelling. Scand. J. Statist., 33:65-81, 2006.
-
(2006)
Scand. J. Statist
, vol.33
, pp. 65-81
-
-
Roverato, A.1
La Rocca, L.2
-
24
-
-
0003614273
-
-
MIT Press, Cambridge, MA, 2nd edition
-
P. Spirtes, C. Glymour, and R. Scheines. Causation, Prediction and Search. MIT Press, Cambridge, MA, 2nd edition, 2000.
-
(2000)
Causation, Prediction and Search
-
-
Spirtes, P.1
Glymour, C.2
Scheines, R.3
-
26
-
-
0031206655
-
A recovery algorithm for chain graphs
-
M. Studený. A recovery algorithm for chain graphs. Int. J. Approx. Reasoning, 17:265-293, 1997.
-
(1997)
Int. J. Approx. Reasoning
, vol.17
, pp. 265-293
-
-
Studený, M.1
-
27
-
-
0032356059
-
On chain graph models for description of conditional independence structures
-
M. Studený and R. R. Bouckaert. On chain graph models for description of conditional independence structures. Ann. Statist., 26:1434-1495, 2001.
-
(2001)
Ann. Statist
, vol.26
, pp. 1434-1495
-
-
Studený, M.1
Bouckaert, R.R.2
-
28
-
-
33746035971
-
The max-min hill-climbing bayesian network structure learning algorithm
-
I. Tsamardinos, L. E. Brown, and C. F. Aliferis. The max-min hill-climbing bayesian network structure learning algorithm. Mach. Learn., 65:31-78, 2006.
-
(2006)
Mach. Learn
, vol.65
, pp. 31-78
-
-
Tsamardinos, I.1
Brown, L.E.2
Aliferis, C.F.3
-
30
-
-
0000243504
-
Graphical and recursive models for contingency tables
-
N. Wermuth and S. L. Lauritzen. Graphical and recursive models for contingency tables. Biometrika, 72:537-552, 1983.
-
(1983)
Biometrika
, vol.72
, pp. 537-552
-
-
Wermuth, N.1
Lauritzen, S.L.2
-
31
-
-
0002515017
-
On substantive research hypotheses, conditional independence graphs and graphical chain models
-
N. Wermuth and S. L. Lauritzen. On substantive research hypotheses, conditional independence graphs and graphical chain models. J. R. Statist. Soc. B, 52:21-50, 1990.
-
(1990)
J. R. Statist. Soc. B
, vol.52
, pp. 21-50
-
-
Wermuth, N.1
Lauritzen, S.L.2
-
33
-
-
0001972601
-
The large-sample distribution of the likelihood ratio for testing composite hypotheses
-
S. Wilks. The large-sample distribution of the likelihood ratio for testing composite hypotheses. Ann. Math. Stat., 20:595-601, 1938.
-
(1938)
Ann. Math. Stat
, vol.20
, pp. 595-601
-
-
Wilks, S.1
-
34
-
-
41549133874
-
A recursive method for structural learning of directed acyclic graphs
-
X. Xie and Z. Geng. A recursive method for structural learning of directed acyclic graphs. J. Mach. Learn. Res., 9:459-483, 2008.
-
(2008)
J. Mach. Learn. Res
, vol.9
, pp. 459-483
-
-
Xie, X.1
Geng, Z.2
-
35
-
-
32944455978
-
Decomposition of structural learning about directed acyclic graphs
-
X. Xie, Z. Geng, and Q. Zhao. Decomposition of structural learning about directed acyclic graphs. Artif. Intell., 170:442-439, 2006.
-
(2006)
Artif. Intell
, vol.170
, pp. 442-439
-
-
Xie, X.1
Geng, Z.2
Zhao, Q.3
|