Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations
Machine Learning
2016-09-14 v1 Data Structures and Algorithms
Machine Learning
Abstract
We describe a method to perform functional operations on probability distributions of random variables. The method uses reproducing kernel Hilbert space representations of probability distributions, and it is applicable to all operations which can be applied to points drawn from the respective distributions. We refer to our approach as {\em kernel probabilistic programming}. We illustrate it on synthetic data, and show how it can be used for nonparametric structural equation models, with an application to causal inference.
Cite
@article{arxiv.1501.06794,
title = {Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations},
author = {Bernhard Schölkopf and Krikamol Muandet and Kenji Fukumizu and Jonas Peters},
journal= {arXiv preprint arXiv:1501.06794},
year = {2016}
}