The Causal Interpretation of Bayesian Networks

2008 ◽  
pp. 83-116 ◽  
Author(s):  
Kevin B. Korb ◽  
Ann E. Nicholson
Keyword(s):  
Bayesian Networks ◽  
Causal Interpretation

scholarly journals Imprecise Bayesian Networks as Causal Models

Information ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 211
Author(s):  
David Kinney
Keyword(s):  
Probability Distribution ◽  
Bayesian Networks ◽  
Causal Structure ◽  
Joint Probability ◽  
Joint Probability Distribution ◽  
Imprecise Probabilities ◽  
Causal Interpretation ◽  
Markov Condition ◽  
Bayes Nets ◽  
Causal Representation

This article considers the extent to which Bayesian networks with imprecise probabilities, which are used in statistics and computer science for predictive purposes, can be used to represent causal structure. It is argued that the adequacy conditions for causal representation in the precise context—the Causal Markov Condition and Minimality—do not readily translate into the imprecise context. Crucial to this argument is the fact that the independence relation between random variables can be understood in several different ways when the joint probability distribution over those variables is imprecise, none of which provides a compelling basis for the causal interpretation of imprecise Bayes nets. I conclude that there are serious limits to the use of imprecise Bayesian networks to represent causal structure.

Bayesian Networks Based Reliability Analysis of Phased-Mission Systems

2009 ◽  
Vol 31 (10) ◽  
pp. 1814-1825 ◽  
Author(s):  
Dong LIU ◽  
Chun-Yuan ZHANG ◽  
Wei-Yan XING ◽  
Rui LI
Keyword(s):  
Bayesian Networks ◽  
Reliability Analysis

Quantifying Systemic Risk Using Bayesian Networks

2020 ◽  
Author(s):  
Sumit Sourabh ◽  
Markus Hofer ◽  
Drona Kandhai
Keyword(s):  
Bayesian Networks ◽  
Systemic Risk
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