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Geometric Shrinkage Priors for Kählerian Signal Filters

Department of Applied Mathematics and Statistics, State University of New York (SUNY), StonyBrook, NY 11794, USA
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in MaxEnt 2014, Amboise, France, 21–26 September 2014.
Entropy 2015, 17(3), 1347-1357;
Received: 16 January 2015 / Revised: 11 March 2015 / Accepted: 12 March 2015 / Published: 17 March 2015
(This article belongs to the Special Issue Information, Entropy and Their Geometric Structures)
We construct geometric shrinkage priors for Kählerian signal filters. Based on the characteristics of Kähler manifolds, an efficient and robust algorithm for finding superharmonic priors which outperform the Jeffreys prior is introduced. Several ansätze for the Bayesian predictive priors are also suggested. In particular, the ansätze related to Kähler potential are geometrically intrinsic priors to the information manifold of which the geometry is derived from the potential. The implication of the algorithm to time series models is also provided. View Full-Text
Keywords: Kähler manifold; information geometry; Bayesian prediction; superharmonic prior Kähler manifold; information geometry; Bayesian prediction; superharmonic prior
MDPI and ACS Style

Choi, J.; Mullhaupt, A.P. Geometric Shrinkage Priors for Kählerian Signal Filters. Entropy 2015, 17, 1347-1357.

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