Markov models of non-Gaussian exponentially correlated processes and their applications

S. Primak; V. Lyandres; V. Kontorovich

doi:10.1103/PhysRevE.63.061103

Markov models of non-Gaussian exponentially correlated processes and their applications

S. Primak, V. Lyandres, V. Kontorovich

Department of Electrical & Computer Engineering

Research output: Contribution to journal › Article › peer-review

18 Scopus citations

Abstract

We consider three different methods of generating non-Gaussian Markov processes with given probability density functions and exponential correlation functions. All models are based on stochastic differential equations. A number of analytically treatable examples are considered. The results obtained can be used in different areas such as telecommunications and neurobiology.

Original language	English
Journal	Physical Review E - Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics
Volume	63
Issue number	6
DOIs	https://doi.org/10.1103/PhysRevE.63.061103
State	Published - 1 Jan 2001

ASJC Scopus subject areas

Statistical and Nonlinear Physics
Statistics and Probability
Condensed Matter Physics

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10.1103/PhysRevE.63.061103

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abstract = "We consider three different methods of generating non-Gaussian Markov processes with given probability density functions and exponential correlation functions. All models are based on stochastic differential equations. A number of analytically treatable examples are considered. The results obtained can be used in different areas such as telecommunications and neurobiology.",

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Markov models of non-Gaussian exponentially correlated processes and their applications

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