Uncertainty in Artificial Intelligence
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Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders
Eleni Sgouritsa, Dominik Janzing, Jonas Peters, Bernhard Schoelkopf
Abstract:
We propose a kernel method to identify finite mixtures of nonparametric product distributions. It is based on a Hilbert space embedding of the joint distribution. The rank of the constructed tensor is equal to the number of mixture components. We present an algorithm to recover the components by partitioning the data points into clusters such that the variables are jointly conditionally independent given the cluster. This method can be used to identify finite confounders.
Keywords:
Pages: 556-565
PS Link:
PDF Link: /papers/13/p556-sgouritsa.pdf
BibTex:
@INPROCEEDINGS{Sgouritsa13,
AUTHOR = "Eleni Sgouritsa and Dominik Janzing and Jonas Peters and Bernhard Schoelkopf",
TITLE = "Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders",
BOOKTITLE = "Proceedings of the Twenty-Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-13)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2013",
PAGES = "556--565"
}


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