Abstract
In many experiments randomization is an important part of the protocol, yet precisely the same data could be produced by an experiment in which randomization played no part. From a Bayesian point of view, randomization plays at most a small role. From a classical point of view, randomization is central to ensuring that the long run error rates are controlled as they are claimed to be.
This work has been supported in part by the National Science Foundation STS-9906128 and ITS-0082928, and NASA NCC2-1239.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Fisher, R.A.: The Design of Experiments. Hafner, New York (1971)
Kadane, J., Seidenfeld, T.: Randomization in a Bayesian perspective. Journal of Statistical Planning and Inference 25 (1990) 329–345
Kyburg, Jr., H.E., Teng, C.M.: Uncertain Inference. Cambridge University Press, New York (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kyburg, H.E., Teng, C.M. (2002). Randomization and Uncertain Inference. In: Ishizuka, M., Sattar, A. (eds) PRICAI 2002: Trends in Artificial Intelligence. PRICAI 2002. Lecture Notes in Computer Science(), vol 2417. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45683-X_69
Download citation
DOI: https://doi.org/10.1007/3-540-45683-X_69
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44038-3
Online ISBN: 978-3-540-45683-4
eBook Packages: Springer Book Archive