On the axiomatic approach to the maximum entropy principle of inference.

Please use this identifier to cite or link to this item: http://hdl.handle.net/2289/1106

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DC Field	Value	Language
dc.contributor.author	Karbelkar, S.N.	-
dc.date.accessioned	2006-01-06T05:34:37Z	-
dc.date.available	2006-01-06T05:34:37Z	-
dc.date.issued	1986-04	-
dc.identifier.citation	Pramana, 1986, Vol. 26, p301-310.	en
dc.identifier.issn	0304-4289	-
dc.identifier.uri	http://hdl.handle.net/2289/1106	-
dc.description.abstract	Recent axiomatic derivations of the maximum entropy principle from consistency conditions are critically examined. We show that proper application of consistency conditions alone allows a wider class of functional& essentially of the form ∫dx [p(x)/g(x)]², for some real number s, to be used for inductive inference and the commonly used form - ∫dx p(x) In [p(x) / g (x)] is only a particular case. The role of the prior density g (x) is clarified. It is possible to regard it as a geometric factor, describing the coordinate system used and it does not represent information of the same kind as obtained by measurements on the system in the form of expectation values.	en
dc.format.extent	461078 bytes	-
dc.format.mimetype	application/pdf	-
dc.language.iso	en	en
dc.publisher	Indian Academy of Sciences, Bangalore, India.	en
dc.rights	Indian Academy of Sciences, Bangalore, India.	en
dc.subject	Inductive inference	en
dc.subject	Maximum entropy principle	en
dc.subject	Prior distribution	en
dc.title	On the axiomatic approach to the maximum entropy principle of inference.	en
dc.type	Article	en
Appears in Collections:	Research Papers (LAMP)

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