On the axiomatic approach to the maximum entropy principle of inference.
| 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.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.identifier.citation | Pramana, 1986, Vol. 26, p301-310. | en |
| dc.identifier.issn | 0304-4289 | |
| dc.identifier.uri | http://hdl.handle.net/2289/1106 | |
| 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 |

