Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders

E. Sgouritsa; D. Janzing; Jonas Martin Peters; B. Schölkopf

Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders

E. Sgouritsa, D. Janzing, Jonas Martin Peters, B. Schölkopf

4 Citations (Scopus)

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.

Original language	Undefined/Unknown
Title of host publication	Proceedings of the 29th Annual Conference on Uncertainty in Artificial Intelligence (UAI)
Number of pages	10
Publication date	2013
Pages	556-565
Publication status	Published - 2013
Externally published	Yes

Cite this

Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders. / Sgouritsa, E.; Janzing, D.; Peters, Jonas Martin et al.

Proceedings of the 29th Annual Conference on Uncertainty in Artificial Intelligence (UAI). 2013. p. 556-565.

Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review

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