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Dynamics of the Fisher information metric

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journal contribution
posted on 2023-06-07, 19:16 authored by Xavier CalmetXavier Calmet, Jaques Calmet
We present a method to generate probability distributions that correspond to metrics obeying partial differential equations generated by extremizing a functional J[g(mu nu)(theta(i))], where g(mu nu)(theta(i)) is the Fisher metric. We postulate that this functional of the dynamical variable g(mu nu)(theta(i)) is stationary with respect to small variations of these variables. Our approach enables a dynamical approach to the Fisher information metric. It allows one to impose symmetries on a statistical system in a systematic way.

History

Publication status

  • Published

File Version

  • Published version

Journal

Physical Review E

ISSN

1539-3755

Publisher

American Physical Society

Issue

5

Volume

71

Department affiliated with

  • Physics and Astronomy Publications

Notes

Article Number: 056109 Part: Part 2

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2012-02-06

First Open Access (FOA) Date

2016-03-22

First Compliant Deposit (FCD) Date

2017-03-10

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