scholarly journals An efficient maximum entropy technique for 2-D isotropic random fields

1988 ◽  
Vol 36 (5) ◽  
pp. 797-812 ◽  
Author(s):  
A.H. Tewfik ◽  
B.C. Levy ◽  
A.S. Willsky
Keyword(s):  
Maximum Entropy ◽  
Random Fields ◽  
Entropy Technique ◽  
Isotropic Random

scholarly journals Needlet approximation for isotropic random fields on the sphere

2017 ◽  
Vol 216 ◽  
pp. 86-116 ◽  
Author(s):  
Quoc T. Le Gia ◽  
Ian H. Sloan ◽  
Yu Guang Wang ◽  
Robert S. Womersley
Keyword(s):  
Random Fields ◽  
Isotropic Random

Comparing HMM, maximum entropy, and conditional random fields for disfluency detection

2005 ◽  
Author(s):  
Yang Liu ◽  
Elizabeth Shriberg ◽  
Andreas Stolcke ◽  
Mary Harper
Keyword(s):  
Maximum Entropy ◽  
Random Fields ◽  
Conditional Random Fields

scholarly journals Identification and isotropy characterization of deformed random fields through excursion sets

2018 ◽  
Vol 50 (3) ◽  
pp. 706-725
Author(s):  
Julie Fournier
Keyword(s):  
Random Field ◽  
Euler Characteristic ◽  
Random Fields ◽  
Weak Form ◽  
Invariance Property ◽  
Basic Domain ◽  
Excursion Sets ◽  
The Mean ◽  
Isotropic Random

Abstract A deterministic application θ:ℝ2→ℝ2 deforms bijectively and regularly the plane and allows the construction of a deformed random field X∘θ:ℝ2→ℝ from a regular, stationary, and isotropic random field X:ℝ2→ℝ. The deformed field X∘θ is, in general, not isotropic (and not even stationary), however, we provide an explicit characterization of the deformations θ that preserve the isotropy. Further assuming that X is Gaussian, we introduce a weak form of isotropy of the field X∘θ, defined by an invariance property of the mean Euler characteristic of some of its excursion sets. We prove that deformed fields satisfying this property are strictly isotropic. In addition, we are able to identify θ, assuming that the mean Euler characteristic of excursion sets of X∘θ over some basic domain is known.

Markov Chains in Many Dimensions

1994 ◽  
Vol 26 (3) ◽  
pp. 756-774 ◽  
Author(s):  
Dimitris N. Politis
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
Maximum Entropy ◽  
Stationary Distribution ◽  
Random Fields ◽  
Special Class ◽  
Markov Random Fields ◽  
One Dimension ◽  
Stationary Markov Chain ◽  
Markov Random

A generalization of the notion of a stationary Markov chain in more than one dimension is proposed, and is found to be a special class of homogeneous Markov random fields. Stationary Markov chains in many dimensions are shown to possess a maximum entropy property, analogous to the corresponding property for Markov chains in one dimension. In addition, a representation of Markov chains in many dimensions is provided, together with a method for their generation that converges to their stationary distribution.

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