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Inference in Multi-Agent Causal Models  (Make Corrections)  
Sam Maes and Stijn Meganck and Bernard Manderick...



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Abstract: In this article we demonstrate the usefulness of causal Bayesian networks as probabilistic reasoning systems. The biggest advantage of causal Bayesian networks over traditional probabilistic Bayesian networks is that they sometimes allow to perform causal inference, i.e. the calculation of the causal e#ect of one variable on other variables. We treat a state-of-the-art algorithm for performing causal inference that is based on a new factorization of the joint probability distribution and is a... (Update)

Active bibliography (related documents):   More   All
0.6:   Identification of Causal Effects in Multi-Agent Causal Models - Maes, Meganck, al. (2005)   (Correct)
0.5:   Learning with Hidden Variables: A Parameter Reusing Approach.. - Karciauskas (2005)   (Correct)
0.4:   Distributed learning of Multi-Agent Causal Models - Stijn Meganck Sam (2005)   (Correct)

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BibTeX entry:   (Update)

@misc{ and-inference,
  author = "Sam Maes And",
  title = "Inference in Multi-Agent Causal Models.",
  url = "citeseer.ist.psu.edu/749029.html" }
Citations (may not include all citations):
760   Probabilistic Reasoning in Intelligent Systems (context) - Pearl - 1988
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3   Causal inference in multi-agent causal models (context) - Maes, Meganck et al. - 2005
2   Handbook of Computational Statistics (context) - Boyens, Gunther et al. - 2004
2   ects in multiagent causal models (context) - Maes, Meganck et al. - 2005
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2   ects in a multiagent causal model (context) - Maes, Reumers et al. - 2003
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1   A general identification condition for causal e#ects (context) - Tian, Pearl - 2002
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1   Multi-agent identification of causal e#ects (context) - Maes, Meganck et al. - 2004
1   Multi-agent causal models: Inference and learning (context) - Maes - 2005
1   Identification in chain multi-agent causal models - Maes, Meganck et al. - 2005
1   Distributed learning of multiagent causal models - Meganck, Maes et al. - 2005

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