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

16 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
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

Access to Document

10.1103/PhysRevE.63.061103

Cite this

@article{a3538fe12d0e4a1db55dd668a8267bd4,

title = "Markov models of non-Gaussian exponentially correlated processes and their applications",

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.",

author = "S. Primak and V. Lyandres and V. Kontorovich",

year = "2001",

month = jan,

day = "1",

doi = "10.1103/PhysRevE.63.061103",

language = "English",

volume = "63",

journal = "Physical Review E",

issn = "2470-0045",

publisher = "American Physical Society",

number = "6",

}

TY - JOUR

T1 - Markov models of non-Gaussian exponentially correlated processes and their applications

AU - Primak, S.

AU - Lyandres, V.

AU - Kontorovich, V.

PY - 2001/1/1

Y1 - 2001/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0035364482&partnerID=8YFLogxK

U2 - 10.1103/PhysRevE.63.061103

DO - 10.1103/PhysRevE.63.061103

M3 - Article

AN - SCOPUS:0035364482

VL - 63

JO - Physical Review E

JF - Physical Review E

SN - 2470-0045

IS - 6

ER -

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

Abstract

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this