Jin Tian
Jin Tian
iastate.edu üzerinde doğrulanmış e-posta adresine sahip - Ana Sayfa
Başlık
Alıntı yapanlar
Alıntı yapanlar
Yıl
A general identification condition for causal effects
J Tian, J Pearl
Aaai/iaai, 567-573, 2002
2832002
Probabilities of causation: Bounds and identification
J Tian, J Pearl
Annals of Mathematics and Artificial Intelligence 28 (1-4), 287-313, 2000
1352000
On the testable implications of causal models with hidden variables
J Tian, J Pearl
arXiv preprint arXiv:1301.0608, 2012
1232012
Causal discovery from changes
J Tian, J Pearl
arXiv preprint arXiv:1301.2312, 2013
1182013
Graphical models for inference with missing data
K Mohan, J Pearl, J Tian
Advances in neural information processing systems, 1277-1285, 2013
118*2013
Bounds on direct effects in the presence of confounded intermediate variables
Z Cai, M Kuroki, J Pearl, J Tian
Biometrics 64 (3), 695-701, 2008
1032008
Finding minimal d-separators
J Tian, A Paz, J Pearl
Computer Science Department, University of California, 1998
88*1998
Recovering from Selection Bias in Causal and Statistical Inference.
E Bareinboim, J Tian, J Pearl
AAAI, 2410-2416, 2014
862014
A branch-and-bound algorithm for MDL learning Bayesian networks
J Tian
arXiv preprint arXiv:1301.3897, 2013
752013
On the identification of causal effects
J Tian, J Pearl
UCLA Cognitive Systems Laboratory, Technical Report (R-290-L), 2002
512002
Bayesian model averaging using the k-best Bayesian network structures
J Tian, R He, L Ram
Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), 2010
392010
Recovering causal effects from selection bias
E Bareinboim, J Tian
Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015
382015
Identifying dynamic sequential plans
J Tian
Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), 2008
32*2008
Inequality constraints in causal models with hidden variables
C Kang, J Tian
Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), 2006
30*2006
Computing posterior probabilities of structural features in Bayesian networks
J Tian, R He
arXiv preprint arXiv:1205.2612, 2012
292012
Identifying direct causal effects in linear models
J Tian
Proceedings of the National Conference on Artificial Intelligence 20 (1), 346, 2005
292005
Identifying conditional causal effects
J Tian
arXiv preprint arXiv:1207.4161, 2012
272012
Testable implications of linear structural equation models
B Chen, J Tian, J Pearl
Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014
262014
Markov Properties for Linear Causal Models with Correlated Errors.
C Kang, J Tian
Journal of Machine Learning Research 10 (1), 2009
252009
A Hybrid Generative/Discriminative Bayesian Classifier.
C Kang, J Tian
FLAIRS Conference, 562-567, 2006
242006
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Makaleler 1–20