Abstract
Existing algorithms of propagation in belief networks deal with inference of observations when conditional distributions are initially defined per edge. The aim of this paper is to propose a direct method of causal inference of both observations and interventions on the causal belief networks quantified with the belief function theory where conditional beliefs are defined for all parents without having to transform the network into a junction tree. We explain how it is still possible to use the disjunctive rule of combination DRC and the generalized Bayesian theorem GBT to perform this propagation.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Ben Yaghlane, B., Mellouli, K.: Inference in directed evidential networks based on the transferable belief model. International Journal Of Approximate Reasoning 48, 399–418 (2008)
Benferhat, S., Smaoui, S.: Possibilistic causal networks for handling interventions: A new propagation algorithm. In: AAAI Conference on Artificial Intelligence (AAAI), pp. 373–378. AAAI Press (2007)
Boukhris, I., Elouedi, Z., Benferhat, S.: Dealing with external actions in causal belief networks. International Journal Of Approximate Reasoning, 978–999 (2013)
Boussarsar, O., Boukhris, I., Elouedi, Z.: Representing interventional knowledge in causal belief networks: Uncertain conditional distributions per cause. In: Laurent, A., Strauss, O., Bouchon-Meunier, B., Yager, R.R. (eds.) IPMU 2014, Part III. CCIS, vol. 444, pp. 223–232. Springer, Heidelberg (2014)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Pub., San Mateo (1988)
Pearl, J.: Causality: Models, Reasonning and Inference. Cambridge University Press (2000)
Shachter, R.D.: Probabilistic inference and influence diagrams. Operations Research 36, 589–604 (1988)
Shafer, G.: A Mathematical Theory of Evidence. Princeton Univ. Press, Princeton (1976)
Smets, P.: The combination of evidence in the transferable belief model. IEEE Pattern Analysis and Machine Intelligence 12, 447–458 (1990)
Smets, P.: Jeffrey’s rule of conditioning generalized to belief functions. In: Uncertainty in Artificial Intelligence, pp. 500–505 (1993)
Xu, H., Smets, P.: Evidential reasoning with conditional belief functions. In: Uncertainty in Artificial Intelligence, pp. 598–606 (1994)
Xu, H., Smets, P.: Reasoning in evidential networks with conditional belief functions. International Journal of Approximate Reasoning 14, 155–185 (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Boussarsar, O., Boukhris, I., Elouedi, Z. (2014). A Direct Propagation Method in Singly Connected Causal Belief Networks with Conditional Distributions for all Causes. In: Aranda-Corral, G.A., Calmet, J., Martín-Mateos, F.J. (eds) Artificial Intelligence and Symbolic Computation. AISC 2014. Lecture Notes in Computer Science(), vol 8884. Springer, Cham. https://doi.org/10.1007/978-3-319-13770-4_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-13770-4_7
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13769-8
Online ISBN: 978-3-319-13770-4
eBook Packages: Computer ScienceComputer Science (R0)