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Workshop " Conditionals , Information , and Inference " 27 REFLECTIONS ON LOGIC & PROBABILITY IN THE CONTEXT OF CONDITIONALS
- Publisher
- Inderscience Publishers
- Copyright
- Copyright © Inderscience Enterprises Ltd. All rights reserved
- ISSN
- 1756-7017
- eISSN
- 1756-7025
- DOI
- 10.1504/IJIDS.2011.043026
- Publisher site
- See Article on Publisher Site
Abstract
This paper extends maximum entropy estimation of discrete probability distributions to the continuous case. This transition leads to a non-parametric estimation of a probability density function, preserving the maximum entropy principle. Furthermore, the derived density estimate provides a minimum mean integrated square error. In the second step, it is shown how boundary conditions can be included, resulting in a probability density function obeying maximum entropy. The criterion for deviation from a reference distribution is the Kullback-Leibler entropy. It is further shown, how the characteristics of a particular distribution can be preserved by using integration kernels with mimetic properties.
Journal
International Journal of Information and Decision Sciences – Inderscience Publishers
Published: Jan 1, 2011