Abstract
Part I of this book is devoted to showing that the fundamental question outlined above is indeed very general, providing a framework into which several common inferential procedures fit. Since the fundamental question of probabilistic logic differs from that of non-probabilistic logic, different techniques may be required to answer the two kinds of question. While proof techniques are often invoked to answer the questions posed in non-probabilistic logics, in Part II we show that probabilistic networks can help answer the fundamental question of probabilistic logic.
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
Fagin, R. and Halpern, J. Y. (1991). Uncertainty, belief, and probability. Computational Intelligence, 6:160–173.
Fagin, R. and Halpern, J. Y. (1994). Reasoning about knowledge and probability. Journal of ACM, 41(2):340–367.
Fagin, R., Halpern, J. Y., Moses, Y., and Vardi, M. Y. (2003). Reasoning About Knowledge. MIT Press, Cambridge, MA.
Hailperin, T. (1996). Sentential Probability Logic. Lehigh University Press, Bethlehem, PA.
Halpern, J. Y. (2003). Reasoning about Uncertainty. MIT Press, Cambridge, MA.
Halpern, J. Y. and Fagin, R. (1992). Two views of belief: belief as generalized probability and belief as evidence. Artificial Intelligence, 54(3):275–317.
Kersting, K. and Raedt, L. D. (2007). Bayesian logic programming: Theory and tool. In Getoor, L. and Taskar, B., editors, Introduction to Statistical Relational Learning. MIT Press, Cambridge, MA.
Kyburg, Jr., H. E. (1987). Bayesian and non-Bayesian evidential updating. Artificial Intelligence, 31:271–294.
Kyburg, Jr., H. E. and Pittarelli, M. (1996). Set-based Bayesianism. IEEE Transactions on Systems, Man and Cybernetics, 26(3):324–339.
Kyburg, Jr., H. E. and Teng, C. M. (2001). Uncertain Inference. Cambridge University Press, Cambridge, MA.
Neapolitan, R. E. (1990). Probabilistic Reasoning in Expert Systems. Wiley, New York, NY.
Neapolitan, R. E. (2003). Learning Bayesian Networks. Prentice Hall, Upper Saddle River.
Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Francisco, CA.
Shafer, G. (1976). A Mathematical Theory of Evidence. Princeton University Press, Princeton, NJ.
Spirtes, P., Glymour, C., and Scheines, R. (1993). Causation, Prediction, and Search. Springer, New York, NY.
Thrun, S., Burgard, W., and Fox, D. (2005). Probabilistic Robotics. MIT Press, Cambridge, MA.
Walley, P. (1991). Statistical Reasoning with Imprecise Probabilities. Chapman and Hall, London.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media B.V.
About this chapter
Cite this chapter
Haenni, R., Romeijn, JW., Wheeler, G., Williamson, J. (2011). Introduction. In: Probabilistic Logics and Probabilistic Networks. Synthese Library, vol 350. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0008-6_1
Download citation
DOI: https://doi.org/10.1007/978-94-007-0008-6_1
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-0007-9
Online ISBN: 978-94-007-0008-6
eBook Packages: Humanities, Social Sciences and LawPhilosophy and Religion (R0)