-
1
-
-
0037893033
-
Finding a Minimum Circuit in a Graph
-
Alon, I., and Rodeh, M., (1978), “Finding a Minimum Circuit in a Graph,” SIAM Journal on Computing, 7, 413–423.
-
(1978)
SIAM Journal on Computing
, vol.7
, pp. 413-423
-
-
Alon, I.1
Rodeh, M.2
-
2
-
-
0031531764
-
A Characterization of Markov Equivalence Classes for Acyclic Digraphs
-
Andersson, S. A., Madigan, D., and Perlman, M. D., (1997), “A Characterization of Markov Equivalence Classes for Acyclic Digraphs,” Annals of Statistics, 25, 505–541.
-
(1997)
Annals of Statistics
, vol.25
, pp. 505-541
-
-
Andersson, S.A.1
Madigan, D.2
Perlman, M.D.3
-
3
-
-
0030211964
-
Bagging Predictors
-
Breiman, L., (1996), “Bagging Predictors,” Machine Learning, 24, 123–140.
-
(1996)
Machine Learning
, vol.24
, pp. 123-140
-
-
Breiman, L.1
-
4
-
-
84919705538
-
Order-Independent Constraint-Based Causal Structure Learning
-
Colombo, D., and Maathuis, M. H., (2014), “Order-Independent Constraint-Based Causal Structure Learning,” Journal of Machine Learning Research, 15, 3741–3782.
-
(2014)
Journal of Machine Learning Research
, vol.15
, pp. 3741-3782
-
-
Colombo, D.1
Maathuis, M.H.2
-
5
-
-
84870061071
-
Objective Bayes Factors for Gaussian Directed Acyclic Graphical Models
-
Consonni, G., and Rocca, L. L., (2012), “Objective Bayes Factors for Gaussian Directed Acyclic Graphical Models,” Scandinavian Journal of Statistics, 39, 743–756.
-
(2012)
Scandinavian Journal of Statistics
, vol.39
, pp. 743-756
-
-
Consonni, G.1
Rocca, L.L.2
-
6
-
-
0007047929
-
Causal Discovery From a Mixture of Experimental and Observational Data
-
Cooper, G. F., and Yoo, C., (1999), “Causal Discovery From a Mixture of Experimental and Observational Data,” in Fifteenth Conference on Uncertainty in Artificial Intelligence, pp. 116–125.
-
(1999)
Fifteenth Conference on Uncertainty in Artificial Intelligence
, pp. 116-125
-
-
Cooper, G.F.1
Yoo, C.2
-
7
-
-
0003687180
-
-
New York: Springer
-
Cowell, R. G., Dawid, P., Lauritzen, S. L., and Spiegelhalter, D. J., (2007), Probabilistic Networks and Expert Systems, New York:Springer.
-
(2007)
Probabilistic Networks and Expert Systems
-
-
Cowell, R.G.1
Dawid, P.2
Lauritzen, S.L.3
Spiegelhalter, D.J.4
-
8
-
-
79958782255
-
Learning Bayesian Networks: Approaches and Issues
-
Daly, R., Shen, Q., and Aitken, S., (2011), “Learning Bayesian Networks:Approaches and Issues,” The Knowledge Engineering Review, 26, 99–157.
-
(2011)
The Knowledge Engineering Review
, vol.26
, pp. 99-157
-
-
Daly, R.1
Shen, Q.2
Aitken, S.3
-
10
-
-
80053158041
-
Bayesian Structure Learning Using Dynamic Programming and MCMC
-
Eaton, D., and Murphy, K., (2007a), “Bayesian Structure Learning Using Dynamic Programming and MCMC,” in Twenty-Third Conference on Uncertainty in Artificial Intelligence, pp. 101–108.
-
(2007)
Twenty-Third Conference on Uncertainty in Artificial Intelligence
, pp. 101-108
-
-
Eaton, D.1
Murphy, K.2
-
11
-
-
85019019275
-
-
Eaton, D., and Murphy, K. (2007b), “BDAGL,” available at http://www.cs.ubc.ca/murphyk/Software/BDAGL/.
-
(2007)
BDAGL
-
-
Eaton, D.1
Murphy, K.2
-
12
-
-
0002344794
-
Bootstrap Methods: Another Look at the Jackknife
-
Efron, B., (1979), “Bootstrap Methods:Another Look at the Jackknife,” Annals of Statistics, 7, 1–26.
-
(1979)
Annals of Statistics
, vol.7
, pp. 1-26
-
-
Efron, B.1
-
14
-
-
49549100459
-
Learning Causal Bayesian Network Structures From Experimental Data
-
Ellis, B., and Wong, W. H., (2008), “Learning Causal Bayesian Network Structures From Experimental Data,” Journal of the American Statistical Association, 103, 778–789.
-
(2008)
Journal of the American Statistical Association
, vol.103
, pp. 778-789
-
-
Ellis, B.1
Wong, W.H.2
-
15
-
-
0842288337
-
Inferring Cellular Networks Using Probabilistic Graphical Models
-
Friedman, N., (2004), “Inferring Cellular Networks Using Probabilistic Graphical Models,” Science, 303, 799–805.
-
(2004)
Science
, vol.303
, pp. 799-805
-
-
Friedman, N.1
-
16
-
-
0002219642
-
Data Analysis With Bayesian Networks: A Bootstrap Approach
-
Friedman, N., Goldszmidt, M., and Wyner, A., (1999), “Data Analysis With Bayesian Networks:A Bootstrap Approach,” in Fifteenth Conference on Uncertainty in Artificial Intelligence, pp. 196–205.
-
(1999)
Fifteenth Conference on Uncertainty in Artificial Intelligence
, pp. 196-205
-
-
Friedman, N.1
Goldszmidt, M.2
Wyner, A.3
-
17
-
-
0037262841
-
Being Bayesian About Network Structure. A Bayesian Approach to Structure Discovery in Bayesian Networks
-
Friedman, N., and Koller, D., (2003), “Being Bayesian About Network Structure. A Bayesian Approach to Structure Discovery in Bayesian Networks,” Machine Learning, 50, 95–125.
-
(2003)
Machine Learning
, vol.50
, pp. 95-125
-
-
Friedman, N.1
Koller, D.2
-
18
-
-
0033707946
-
Using Bayesian Networks to Analyze Expression Data
-
Friedman, N., Linial, M., Nachman, I., and Pe’er, D., (2000), “Using Bayesian Networks to Analyze Expression Data,” Journal of Computational Biology, 7, 601–620.
-
(2000)
Journal of Computational Biology
, vol.7
, pp. 601-620
-
-
Friedman, N.1
Linial, M.2
Nachman, I.3
Pe’er, D.4
-
19
-
-
0000034390
-
Learning Gaussian Networks
-
Geiger, D., and Heckerman, D., (1994), “Learning Gaussian Networks,” in Tenth Conference on Uncertainty in Artificial Intelligence, pp. 235–243.
-
(1994)
Tenth Conference on Uncertainty in Artificial Intelligence
, pp. 235-243
-
-
Geiger, D.1
Heckerman, D.2
-
20
-
-
0036431552
-
Parameter Priors for Directed Acyclic Graphical Models and the Characterization of Several Probability Distributions
-
Geiger, D., and Heckerman, D. (2002), “Parameter Priors for Directed Acyclic Graphical Models and the Characterization of Several Probability Distributions,” Annals of Statistics, 30, 1412–1440.
-
(2002)
Annals of Statistics
, vol.30
, pp. 1412-1440
-
-
Geiger, D.1
Heckerman, D.2
-
21
-
-
0037266163
-
Improving Markov Chain Monte Carlo Model Search for Data Mining
-
Giudici, P., and Castelo, R., (2003), “Improving Markov Chain Monte Carlo Model Search for Data Mining,” Machine Learning, 50, 127–158.
-
(2003)
Machine Learning
, vol.50
, pp. 127-158
-
-
Giudici, P.1
Castelo, R.2
-
22
-
-
43049097125
-
Improving the Structure MCMC Sampler for Bayesian Networks by Introducing a New Edge Reversal Move
-
Grzegorczyk, M., and Husmeier, D., (2008), “Improving the Structure MCMC Sampler for Bayesian Networks by Introducing a New Edge Reversal Move,” Machine Learning, 71, 265–305.
-
(2008)
Machine Learning
, vol.71
, pp. 265-305
-
-
Grzegorczyk, M.1
Husmeier, D.2
-
23
-
-
84917733558
-
Jointly Interventional and Observational Data: Estimation of Interventional Markov Equivalence Classes of Directed Acyclic Graphs
-
Series B
-
Hauser, A., and Bühlmann, P., (2015), “Jointly Interventional and Observational Data:Estimation of Interventional Markov Equivalence Classes of Directed Acyclic Graphs,” Journal of the Royal Statistical Society, Series B, 77, 291–318.
-
(2015)
Journal of the Royal Statistical Society
, vol.77
, pp. 291-318
-
-
Hauser, A.1
Bühlmann, P.2
-
24
-
-
85019039843
-
-
ArXiv:1501.04370
-
He, R., Tian, J., and Wu, H., (2015), “Structure Learning in Bayesian Networks of Moderate Size By Efficient Sampling,” ArXiv:1501.04370.
-
(2015)
Structure Learning in Bayesian Networks of Moderate Size By Efficient Sampling
-
-
He, R.1
Tian, J.2
Wu, H.3
-
25
-
-
84885027886
-
Reversible MCMC on Markov Equivalence Classes of Sparse Directed Acyclic Graphs
-
He, Y., Jia, J., and Yu, B., (2013), “Reversible MCMC on Markov Equivalence Classes of Sparse Directed Acyclic Graphs,” Annals of Statistics, 41, 1742–1779.
-
(2013)
Annals of Statistics
, vol.41
, pp. 1742-1779
-
-
He, Y.1
Jia, J.2
Yu, B.3
-
26
-
-
0003846045
-
Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains
-
Heckerman, D., and Geiger, D., (1995), “Learning Bayesian Networks:A Unification for Discrete and Gaussian Domains,” in Eleventh Conference on Uncertainty in Artificial Intelligence, pp. 274–284.
-
(1995)
Eleventh Conference on Uncertainty in Artificial Intelligence
, pp. 274-284
-
-
Heckerman, D.1
Geiger, D.2
-
27
-
-
84868020742
-
Bayesian Inference of Signaling Network Topology in a Cancer Cell Line
-
Hill, S. M., Lu, Y., Molina, J., Heiserand, L. M., Spellman, P. T., Speed, T. P., Gray, J. W., Mills, G. B., and Mukherjee, S., (2012), “Bayesian Inference of Signaling Network Topology in a Cancer Cell Line,” Bioinformatics, 28, 2804–2810.
-
(2012)
Bioinformatics
, vol.28
, pp. 2804-2810
-
-
Hill, S.M.1
Lu, Y.2
Molina, J.3
Heiserand, L.M.4
Spellman, P.T.5
Speed, T.P.6
Gray, J.W.7
Mills, G.B.8
Mukherjee, S.9
-
28
-
-
0344464762
-
Sensitivity and Specificity of Inferring Genetic Regulatory Interactions From Microarray Experiments With Dynamic Bayesian Networks
-
Husmeier, D., (2003), “Sensitivity and Specificity of Inferring Genetic Regulatory Interactions From Microarray Experiments With Dynamic Bayesian Networks,” Bioinformatics, 19, 2271–2282.
-
(2003)
Bioinformatics
, vol.19
, pp. 2271-2282
-
-
Husmeier, D.1
-
29
-
-
33947524259
-
Estimating High-Dimensional Directed Acyclic Graphs With the PC-Algorithm
-
Kalisch, M., and Bühlmann, P., (2007), “Estimating High-Dimensional Directed Acyclic Graphs With the PC-Algorithm,” Journal of Machine Learning Research, 8, 613–636.
-
(2007)
Journal of Machine Learning Research
, vol.8
, pp. 613-636
-
-
Kalisch, M.1
Bühlmann, P.2
-
30
-
-
84863330390
-
Causal Inference Using Graphical Models With the R Package Pcalg
-
Kalisch, M., Mächler, M., Colombo, D., Maathuis, M. H., and Bühlmann, P., (2012), “Causal Inference Using Graphical Models With the R Package Pcalg,” Journal of Statistical Software, 47, 1–26.
-
(2012)
Journal of Statistical Software
, vol.47
, pp. 1-26
-
-
Kalisch, M.1
Mächler, M.2
Colombo, D.3
Maathuis, M.H.4
Bühlmann, P.5
-
31
-
-
31844439894
-
Exact Bayesian Structure Discovery in Bayesian Networks
-
Koivisto, M., and Sood, K., (2004), “Exact Bayesian Structure Discovery in Bayesian Networks,” Journal of Machine Learning Research, 5, 549–573.
-
(2004)
Journal of Machine Learning Research
, vol.5
, pp. 549-573
-
-
Koivisto, M.1
Sood, K.2
-
32
-
-
70649111792
-
-
Cambridge, MA: MIT Press
-
Koller, D., and Friedman, N., (2009), Probabilistic Graphical Models, Cambridge, MA:MIT Press.
-
(2009)
Probabilistic Graphical Models
-
-
Koller, D.1
Friedman, N.2
-
33
-
-
84886987303
-
Uniform Random Generation of Large Acyclic Digraphs
-
Kuipers, J., and Moffa, G., (2015), “Uniform Random Generation of Large Acyclic Digraphs,” Statistics and Computing, 25, 227–242.
-
(2015)
Statistics and Computing
, vol.25
, pp. 227-242
-
-
Kuipers, J.1
Moffa, G.2
-
34
-
-
84979953170
-
Addendum on the Scoring of Gaussian Directed Acyclic Graphical Models
-
Kuipers, J., Moffa, G., and Heckerman, D., (2014), “Addendum on the Scoring of Gaussian Directed Acyclic Graphical Models,” Annals of Statistics, 42, 1689–1691.
-
(2014)
Annals of Statistics
, vol.42
, pp. 1689-1691
-
-
Kuipers, J.1
Moffa, G.2
Heckerman, D.3
-
36
-
-
84867407667
-
On Thinning of Chains in MCMC
-
Link, W. A., and Eaton, M. J., (2012), “On Thinning of Chains in MCMC,” Methods in Ecology and Evolution, 3, 112–115.
-
(2012)
Methods in Ecology and Evolution
, vol.3
, pp. 112-115
-
-
Link, W.A.1
Eaton, M.J.2
-
37
-
-
69949166983
-
Estimating High-Dimensional Intervention Effects From Observational Data
-
Maathuis, M. H., Kalisch, M., and Bühlmann, P., (2009), “Estimating High-Dimensional Intervention Effects From Observational Data,” Annals of Statistics, 37, 3133–3164.
-
(2009)
Annals of Statistics
, vol.37
, pp. 3133-3164
-
-
Maathuis, M.H.1
Kalisch, M.2
Bühlmann, P.3
-
38
-
-
0000220791
-
Bayesian Model Averaging and Model Selection for Markov Equivalence Classes of Acyclic Digraphs
-
Madigan, D., Andersson, S. A., Perlman, M. D., and Volinsky, C. T., (1996), “Bayesian Model Averaging and Model Selection for Markov Equivalence Classes of Acyclic Digraphs,” Communications in Statistics — Theory and Methods, 25, 2493–2519.
-
(1996)
Communications in Statistics — Theory and Methods
, vol.25
, pp. 2493-2519
-
-
Madigan, D.1
Andersson, S.A.2
Perlman, M.D.3
Volinsky, C.T.4
-
39
-
-
21844520724
-
Bayesian Graphical Models for Discrete Data
-
Madigan, D., and York, J., (1995), “Bayesian Graphical Models for Discrete Data,” International Statistical Review, 63, 215–232.
-
(1995)
International Statistical Review
, vol.63
, pp. 215-232
-
-
Madigan, D.1
York, J.2
-
40
-
-
34247101935
-
Random Generation of Directed Acyclic Graphs
-
Melançon, G., Dutour, I., and Bousquet-Mélou, M., (2001), “Random Generation of Directed Acyclic Graphs,” Electronic Notes in Discrete Mathematics, 10, 202–207.
-
(2001)
Electronic Notes in Discrete Mathematics
, vol.10
, pp. 202-207
-
-
Melançon, G.1
Dutour, I.2
Bousquet-Mélou, M.3
-
41
-
-
1842736912
-
Generating Connected Acyclic Digraphs Uniformly at Random
-
Melançon, G., and Philippe, F., (2004), “Generating Connected Acyclic Digraphs Uniformly at Random,” Information Processing Letters, 90, 209–213.
-
(2004)
Information Processing Letters
, vol.90
, pp. 209-213
-
-
Melançon, G.1
Philippe, F.2
-
42
-
-
55749093996
-
Network Inference Using Informative Priors
-
Mukherjee, S., and Speed, T. P., (2008), “Network Inference Using Informative Priors,” Proceedings of the National Academy of Sciences, 105, 14313–14318.
-
(2008)
Proceedings of the National Academy of Sciences
, vol.105
, pp. 14313-14318
-
-
Mukherjee, S.1
Speed, T.P.2
-
43
-
-
80053156983
-
Partial Order MCMC for Structure Discovery in Bayesian Networks
-
Niinimäki, T., Parviainen, P., and Koivisto, M., (2011), “Partial Order MCMC for Structure Discovery in Bayesian Networks,” in Twenty-seventh Conference on Uncertainty in Artificial Intelligence, pp. 557–564.
-
(2011)
Twenty-seventh Conference on Uncertainty in Artificial Intelligence
, pp. 557-564
-
-
Niinimäki, T.1
Parviainen, P.2
Koivisto, M.3
-
45
-
-
0002838962
-
A Theory of Inferred Causation
-
Pearl, J., and Verma, T. S., (1991), “A Theory of Inferred Causation,” in Second International Conference on Principles of Knowledge Representation and Reasoning, pp. 441–452.
-
(1991)
Second International Conference on Principles of Knowledge Representation and Reasoning
, pp. 441-452
-
-
Pearl, J.1
Verma, T.S.2
-
47
-
-
84923347024
-
Structural Intervention Distance (SID) for Evaluating Causal Graphs
-
Peters, J., and Bühlmann, P., (2015), “Structural Intervention Distance (SID) for Evaluating Causal Graphs,” Neural Computation, 27, 771–799.
-
(2015)
Neural Computation
, vol.27
, pp. 771-799
-
-
Peters, J.1
Bühlmann, P.2
-
48
-
-
84867979927
-
Reverse Engineering Gene Regulatory Networks Using Approximate Bayesian Computation
-
Rau, A., Jaffrézic, F., Foulley, J.-L., and Doerge, R. W., (2012), “Reverse Engineering Gene Regulatory Networks Using Approximate Bayesian Computation,” Statistics and Computing, 22, 1257–1271.
-
(2012)
Statistics and Computing
, vol.22
, pp. 1257-1271
-
-
Rau, A.1
Jaffrézic, F.2
Foulley, J.-L.3
Doerge, R.W.4
-
50
-
-
0001457227
-
Counting Labeled Acyclic Digraphs
-
ed. F. Harary, New York: Academic Press
-
Robinson, R. W., (1973), “Counting Labeled Acyclic Digraphs,” in New Directions in the Theory of Graphs, ed. F. Harary, New York:Academic Press, pp. 239–273.
-
(1973)
New Directions in the Theory of Graphs
, pp. 239-273
-
-
Robinson, R.W.1
-
51
-
-
17644427718
-
Causal Protein-Signaling Networks Derived From Multiparameter Single-Cell Data
-
Sachs, K., Perez, O., Pe’er, D., Lauffenburger, D. A., and Nolan, G. P., (2005), “Causal Protein-Signaling Networks Derived From Multiparameter Single-Cell Data,” Science, 308, 523–529.
-
(2005)
Science
, vol.308
, pp. 523-529
-
-
Sachs, K.1
Perez, O.2
Pe’er, D.3
Lauffenburger, D.A.4
Nolan, G.P.5
-
52
-
-
77955124773
-
Learning Bayesian Networks With the bnlearn R Package
-
Scutari, M., (2010), “Learning Bayesian Networks With the bnlearn R Package,” Journal of Statistical Software, 35, 1–22.
-
(2010)
Journal of Statistical Software
, vol.35
, pp. 1-22
-
-
Scutari, M.1
-
53
-
-
0003614273
-
-
Cambridge, MA: MIT Press
-
Spirtes, P., Glymour, C. N., and Scheines, R., (2000), Causation, Prediction, and Search, Cambridge, MA:MIT Press.
-
(2000)
Causation, Prediction, and Search
-
-
Spirtes, P.1
Glymour, C.N.2
Scheines, R.3
-
54
-
-
0000192604
-
Acyclic Orientations of Graphs
-
Stanley, R. P., (1973), “Acyclic Orientations of Graphs,” Discrete Mathematics, 5, 171–178.
-
(1973)
Discrete Mathematics
, vol.5
, pp. 171-178
-
-
Stanley, R.P.1
-
55
-
-
33746035971
-
The Max-Min Hill-Climbing Bayesian Network Structure Learning Algorithm
-
Tsamardinos, I., Brown, L. E., and Aliferis, C. F., (2006), “The Max-Min Hill-Climbing Bayesian Network Structure Learning Algorithm,” Machine Learning, 65, 31–78.
-
(2006)
Machine Learning
, vol.65
, pp. 31-78
-
-
Tsamardinos, I.1
Brown, L.E.2
Aliferis, C.F.3
-
56
-
-
0002095306
-
Equivalence and Synthesis of Causal Models
-
Verma, T. S., and Pearl, J., (1990), “Equivalence and Synthesis of Causal Models,” in Sixth Conference on Uncertainty in Artificial Intelligence, pp. 220–227.
-
(1990)
Sixth Conference on Uncertainty in Artificial Intelligence
, pp. 220-227
-
-
Verma, T.S.1
Pearl, J.2
-
57
-
-
34249774309
-
Reconstructing Gene Regulatory Networks with Bayesian Networks by Combining Expression Data With Multiple Sources of Prior Knowledge
-
Werhli, A. V., and Husmeier, D., (2007), “Reconstructing Gene Regulatory Networks with Bayesian Networks by Combining Expression Data With Multiple Sources of Prior Knowledge,” Statistical Applications in Genetics and Molecular Biology, 6, 1–47.
-
(2007)
Statistical Applications in Genetics and Molecular Biology
, vol.6
, pp. 1-47
-
-
Werhli, A.V.1
Husmeier, D.2
|