Abstract
Often the primary objective of constructing a model from the data is to model the phenomenonthat produces the data in such a way that the model is useful for the desired type of reasoning objectives. In many graph models of probabilistic knowledge various aspects of these phenomena are represented by nodes while the edges represent the probabilistic dependencies among them. We demonstrate one type of reasoning objective that is better served by those models in which some qualitative relationships of the phenomena, are used as the edges and the hyperedges of the graph models. We have also outlined our methods for handling such models for reasoning and for learning the qualitative relationships of a domain from the empirical data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Raj Bhatnagar and Laveen N Kanal. Structural and Probabilistic Knowledge for Abductive Reasoning, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, No. 3, pp 233–245.
G. F. Cooper and E. Herskovits. A Bayesian Method for Constructing Bayesian Belief Networks from Databases. Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence, 1991, pp. 86–94.
Johan de Kleer and Brian C. Williams. Diagnosis With Behavioral Modes, Proceedings of the IJCAI, 1989 Morgan Kaufmann, pp 1324–1330.
E. Herskovits and G. Cooper. An Entropy-Driven System for Construction of Probabilistic Expert Systems from Databases. Uncertainty in Artificial Intelligence 6, P. P. Bonissone, M. Henrion, L. N. Kanal and J. F. Lemmer (eds), North Holland, 1991, pp 117–125.
S. L. Lauritzen and D. J. Spiegelhalter, Local Computations with Probabilities on Graphical Structures and their Application to Expert Systems, The Journal of Royal Statistical Society, Series B, vol. 50, No. 2, pp 157–224, 1988.
Richard E. Neapolitan Probabilistic Reasoning in Expert Systems, John Wiley and Sons, Inc. 1990.
Judea Pearl. Fusion, Propagation, and Structuring in Bayesian Networks, Artificial Intelligence vol. 29, 1986, pp 241–288.
Judea Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference., Morgan Kauffman publishers, 1988.
J. Pearl, T. S. Verma. A Theory of Inferred Causation. Principles of Knowledge Representation and Reasoning: Proceedings of the Second International Conference, Morgan Kaufmann. ( April, 1991 ).
J. R. Quinlan. Induction of Decision Trees, Machine Learning vol. 1, number 1, pp 81–106, 1986.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1994 Springer-Verlag New York, Inc.
About this paper
Cite this paper
Bhatnagar, R., Kanal, L.N. (1994). Models from Data for Various Types of Reasoning. In: Cheeseman, P., Oldford, R.W. (eds) Selecting Models from Data. Lecture Notes in Statistics, vol 89. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2660-4_18
Download citation
DOI: https://doi.org/10.1007/978-1-4612-2660-4_18
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-94281-0
Online ISBN: 978-1-4612-2660-4
eBook Packages: Springer Book Archive