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Combining Experiments to Discover Linear Cyclic Models with Latent Variables.

, , and . AISTATS, volume 9 of JMLR Proceedings, page 185-192. JMLR.org, (2010)

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Natural Image Statistics - A Probabilistic Approach to Early Computational Vision, , and . Computational Imaging and Vision Springer, (2009)Experiment selection for causal discovery., , and . J. Mach. Learn. Res., 14 (1): 3041-3071 (2013)Learning linear cyclic causal models with latent variables., , and . J. Mach. Learn. Res., (2012)Causal modelling combining instantaneous and lagged effects: an identifiable model based on non-Gaussianity., , and . ICML, volume 307 of ACM International Conference Proceeding Series, page 424-431. ACM, (2008)Data-driven covariate selection for nonparametric estimation of causal effects., , and . AISTATS, volume 31 of JMLR Workshop and Conference Proceedings, page 256-264. JMLR.org, (2013)Sparse coding of natural contours., and . Neurocomputing, (2002)Noisy-OR Models with Latent Confounding., , and . UAI, page 363-372. AUAI Press, (2011)Finding a causal ordering via independent component analysis., , , and . Comput. Stat. Data Anal., 50 (11): 3278-3293 (2006)Interpreting Neural Response Variability as Monte Carlo Sampling of the Posterior., and . NIPS, page 277-284. MIT Press, (2002)Independent subspace analysis shows emergence of phase and shift invariant features from natural images., and . IJCNN, page 1059-1064. IEEE, (1999)