Computing functions of random variables via reproducing kernel Hilbert space representations

作     者：Schoelkopf, Bernhard Muandet, Krikamol Fukumizu, Kenji Harmeling, Stefan Peters, Jonas 

作者机构：Max Planck Inst Intelligent Syst D-72076 Tubingen Germany Inst Stat Math Tachikawa Tokyo Japan Univ Dusseldorf Inst Informat D-40225 Dusseldorf Germany Swiss Fed Inst Technol Seminar Stat CH-8092 Zurich Switzerland 

出 版 物：《STATISTICS AND COMPUTING》 (统计学与计算)

年 卷 期：2015年第25卷第4期

页      面：755-766页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Kernel methods Probabilistic programming Causal inference 

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.