Title:
Discriminating between Causal Structures in Bayesian Networks Given Partial Observations
Author(s):
Philip Moritz, Jörg Reichardt, Nihat Ay
Paper #:
13-03-010
Date:
March 13, 2013
Abstract:
Given an observed subset Y1 , . . . , Yk of variables in a fixed Bayesian network G, our main result is a tight bound on the generalized mutual information Ic(Y1,...,Yk) = ∑ j =1kH(Yj)/c − H(Y1, . . . ,Yk) over all probability distributions satisfying G. Our bound depends on the ancestral structure of the nodes in the network. It makes it possible to discriminate between different causal models for a probability distribution, as we show from numerical experiments.
Facebook
Twitter
LinkedIn
Email Newsletter
SFI News Feed
Google+
YouTube
iTunes U