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Samuel Elogne

dblp:07/4611 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2007
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Theory of computation · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Probabilistic and Bayesian machine learning · 100%
Theoretical computer science
1 paper
Information theory · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Probabilistic and Bayesian machine learning › stochastic processes
gaussian process
0.112007
Analytic Properties and Covariance Functions for a New Class of Generalized Gibbs Random Fields · IEEE Trans. Inf. Theory 2007
Information theory › probability theory › stochastic processes › stochastic process representation
covariance function
0.112007
Analytic Properties and Covariance Functions for a New Class of Generalized Gibbs Random Fields · IEEE Trans. Inf. Theory 2007

Methods — techniques the papers use, named apart from their topics

coarse-graining kernel · 0.1asymptotic analysis · 0.1
YearPublicationVenuePosition
2007 Analytic Properties and Covariance Functions for a New Class of Generalized Gibbs Random Fields
abstract
Spartan spatial random fields (SSRFs) are generalized Gibbs random fields, equipped with a coarse-graining kernel that acts as a low-pass filter for the fluctuations. SSRFs are defined by means of physically motivated spatial interactions and a small set of free parameters (interaction couplings). This paper focuses on the fluctuation–gradient–curvature (FGC) SSRF model, henceforth referred to as FGC-SSRF. This model is defined on the Euclidean space$\BBR ^{d}$by means of interactions proportional to the squares of the field realizations, as well as their gradient and curvature. The permissibility criteria of FGC-SSRFs are extended by considering the impact of a finite-bandwidth kernel. It is proved that the FGC-SSRFs are almost surely differentiable in the case of finite bandwidth. Asymptotic explicit expressions for the Spartan covariance function are derived for$d=1$and$d=3$; both known and new covariance functions are obtained depending on the value of the FGC-SSRF shape parameter. Nonlinear dependence of the covariance integral scale on the FGC-SSRF characteristic length is established, and it is shown that the relation becomes linear asymptotically. The results presented in this paper are useful in random field parameter inference, and in spatial interpolation of irregularly spaced samples.
Dionissios T. Hristopulos, Samuel Elogne
IEEE Trans. Inf. Theory2