Abstract

Originally coming from physics, maximum entropy (ME) has been promoted to a general principle of inference primarily by the works of Jaynes. ME applies to the problem of inferring a probability mass (or density) function, or any non-negative function p(x), when the available information specifies a set E of feasible functions, and there is a prior guess q /spl notin/ E. The author will review the arguments that have been put forward for justifying ME. In this author's opinion, the strongest theoretical support to ME is provided by the axiomatic approach. This shows that, in some sense, ME is the only logically consistent method of inferring a function subject to linear constraints.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.