Computing functions of random variables via reproducing kernel Hilbert space representations

被引：24

作者：

Schoelkopf, Bernhard ^{[1
]}

Muandet, Krikamol ^{[1
]}

Fukumizu, Kenji ^{[2
]}

Harmeling, Stefan ^{[3
]}

Peters, Jonas ^{[4
]}

机构：

[1] Max Planck Inst Intelligent Syst, D-72076 Tubingen, Germany

[2] Inst Stat Math, Tachikawa, Tokyo, Japan

[3] Univ Dusseldorf, Inst Informat, D-40225 Dusseldorf, Germany

[4] Swiss Fed Inst Technol, Seminar Stat, CH-8092 Zurich, Switzerland

来源：

STATISTICS AND COMPUTING | 2015年 / 25卷 / 04期

关键词：

Kernel methods; Probabilistic programming; Causal inference; SUPPORT;

D O I：

10.1007/s11222-015-9558-5

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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 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.

引用

页码：755 / 766

页数：12