Jointly interventional and observational data: estimation of interventional Markov equivalence classes of directed acyclic graphs

In many applications we have both observational and (randomized) interventional data. We propose a Gaussian likelihood framework for joint modeling of such different data-types, based on global parameters consisting of a directed acyclic graph (DAG) and correponding edge weights and error variances....

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Veröffentlicht in:	arXiv.org 2013-03
Hauptverfasser:	Hauser, Alain, Bühlmann, Peter
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer simulation Data points Economic models Equivalence Graph theory Markov processes Mathematics - Statistics Theory Maximum likelihood estimation Monte Carlo simulation Parameter estimation Statistics - Methodology Statistics - Theory
Online-Zugang:	Volltext
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