# Discriminating between causal structures in Bayesian Networks given partial observations

Philipp Moritz; Jörg Reichardt; Nihat Ay

Kybernetika (2014)

- Volume: 50, Issue: 2, page 284-295
- ISSN: 0023-5954

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topMoritz, Philipp, Reichardt, Jörg, and Ay, Nihat. "Discriminating between causal structures in Bayesian Networks given partial observations." Kybernetika 50.2 (2014): 284-295. <http://eudml.org/doc/261858>.

@article{Moritz2014,

abstract = {Given a fixed dependency graph $G$ that describes a Bayesian network of binary variables $X_1, \dots , X_n$, our main result is a tight bound on the mutual information $I_c(Y_1, \dots , Y_k) = \sum _\{j=1\}^k H(Y_j)/c - H(Y_1, \dots , Y_k)$ of an observed subset $Y_1, \dots , Y_k$ of the variables $X_1, \dots , X_n$. Our bound depends on certain quantities that can be computed from the connective structure of the nodes in $G$. Thus it allows to discriminate between different dependency graphs for a probability distribution, as we show from numerical experiments.},

author = {Moritz, Philipp, Reichardt, Jörg, Ay, Nihat},

journal = {Kybernetika},

keywords = {Bayesian networks; causal Markov condition; information theory; information inequalities; common ancestors; causal inference; Bayesian networks; causal Markov condition; information theory; information inequalities; common ancestors; causal inference},

language = {eng},

number = {2},

pages = {284-295},

publisher = {Institute of Information Theory and Automation AS CR},

title = {Discriminating between causal structures in Bayesian Networks given partial observations},

url = {http://eudml.org/doc/261858},

volume = {50},

year = {2014},

}

TY - JOUR

AU - Moritz, Philipp

AU - Reichardt, Jörg

AU - Ay, Nihat

TI - Discriminating between causal structures in Bayesian Networks given partial observations

JO - Kybernetika

PY - 2014

PB - Institute of Information Theory and Automation AS CR

VL - 50

IS - 2

SP - 284

EP - 295

AB - Given a fixed dependency graph $G$ that describes a Bayesian network of binary variables $X_1, \dots , X_n$, our main result is a tight bound on the mutual information $I_c(Y_1, \dots , Y_k) = \sum _{j=1}^k H(Y_j)/c - H(Y_1, \dots , Y_k)$ of an observed subset $Y_1, \dots , Y_k$ of the variables $X_1, \dots , X_n$. Our bound depends on certain quantities that can be computed from the connective structure of the nodes in $G$. Thus it allows to discriminate between different dependency graphs for a probability distribution, as we show from numerical experiments.

LA - eng

KW - Bayesian networks; causal Markov condition; information theory; information inequalities; common ancestors; causal inference; Bayesian networks; causal Markov condition; information theory; information inequalities; common ancestors; causal inference

UR - http://eudml.org/doc/261858

ER -

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