Maximum entropy as a feasible way to describe joint distribution in expert systems
© 2017 by the Mathematical Association of Thailand. All rights reserved. In expert systems, we elicit the probabilities of different statements from the experts. However, to adequately use the expert system, we also need to know the probabilities of different propositional combinations of the expert...
Saved in:
Main Authors: | Thongchai Dumrongpokaphan, Vladik Kreinovich, Hung T. Nguyen |
---|---|
Format: | Journal |
Published: |
2018
|
Subjects: | |
Online Access: | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85039713278&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/57553 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Chiang Mai University |
Similar Items
-
Maximum entropy as a feasible way to describe joint distribution in expert systems
by: Thongchai Dumrongpokaphan, et al.
Published: (2018) -
Entropy as a measure of average loss of privacy
by: Luc Longpré, et al.
Published: (2018) -
Entropy as a measure of average loss of privacy
by: Luc Longpré, et al.
Published: (2018) -
Using second-order probabilities to make maximum entropy approach to copulas more reasonable
by: Hung T. Nguyen, et al.
Published: (2018) -
Empirically successful transformations from non-gaussian to close-to-gaussian distributions: Theoretical justification
by: Thongchai Dumrongpokaphan, et al.
Published: (2018)