Abstract
An inference scheme that extends and generalizes both MaxEnt and the Maximum Likelihood Principle is proposed. It incorporates both prior knowledge and sample data but, unlike the Bayesian inference, does not involve distributions on the parameter space. In contrast to the Maximum Likelihood Principle, it produces meaningful estimates even when the size of the sample is small.
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
Preview
Unable to display preview. Download preview PDF.
References
P.J. Bickel and K.A. Doksum. Mathematical Statistics. Basic Ideas and Selected Topics. Holden-Day, San Francisco, 1977.
P.S. Faynzilberg. Acceptable Likelihood and Bayesian Inference with Retrospection. Working Paper 1995-02, Graduate School of Industrial Administration, Carnegie Mellon University, Pittsburgh, Pennsylvania, March 1995.
Amnon Katz. Principles of Statistical Mechanics. W.H. Freeman and Company & Sons, San Francisco, 1967.
E.L. Lehmann. Theory of Point Estimation. Wiley Series in Probability and Mathematical Statistics, John Wiley & Sons, New York, 1983.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1996 Springer Science+Business Media Dordrecht
About this paper
Cite this paper
Faynzilberg, P.S. (1996). Meal Estimation: Acceptable-Likelihood Extensions of Maxent. In: Hanson, K.M., Silver, R.N. (eds) Maximum Entropy and Bayesian Methods. Fundamental Theories of Physics, vol 79. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-5430-7_49
Download citation
DOI: https://doi.org/10.1007/978-94-011-5430-7_49
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-010-6284-8
Online ISBN: 978-94-011-5430-7
eBook Packages: Springer Book Archive