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Structured dependence between stochastic processes

Record Type:	Electronic resources : Monograph/item
Title/Author:	Structured dependence between stochastic processesTomasz R. Bielecki, Jacek Jakubowski, Mariusz Niewęgłowski
Author:	Bielecki, Tomasz R.
other author:	Jakubowski, Jacek.
Published:	Cambridge :Cambridge University Press,2020.
Description:	viii, 269 p. :ill., digital ;25 cm.
Notes:	Title from publisher's bibliographic system (viewed on 21 Sep 2020).
Subject:	Markov processes.
Online resource:	https://doi.org/10.1017/9781316650530
ISBN:	9781316650530$q(electronic bk.)

Structured dependence between stochastic processes
Bielecki, Tomasz R.

Structured dependence between stochastic processes[electronic resource] /Tomasz R. Bielecki, Jacek Jakubowski, Mariusz Niewęgłowski - Cambridge :Cambridge University Press,2020. - viii, 269 p. :ill., digital ;25 cm. - Encyclopedia of mathematics and its applications ;175. - Encyclopedia of mathematics and its applications ;v. 124-125..

Title from publisher's bibliographic system (viewed on 21 Sep 2020).

Strong Markov consistency of multivariate Markov families and processes -- Consistency of finite multivariate Markov chains -- Consistency of finite multivariate conditional Markov chains -- Consistency of multivariate special semimartingales -- Strong Markov family structures -- Markov chain structures -- Conditional Markov chain structures -- Special semimartingale structures -- Archimedean survival processes, Markov consistency, ASP structures -- Generalized multivariate Hawkes processes -- Applications of stochastic structures.

The relatively young theory of structured dependence between stochastic processes has many real-life applications in areas including finance, insurance, seismology, neuroscience, and genetics. With this monograph, the first to be devoted to the modeling of structured dependence between random processes, the authors not only meet the demand for a solid theoretical account but also develop a stochastic processes counterpart of the classical copula theory that exists for finite-dimensional random variables. Presenting both the technical aspects and the applications of the theory, this is a valuable reference for researchers and practitioners in the field, as well as for graduate students in pure and applied mathematics programs. Numerous theoretical examples are included, alongside examples of both current and potential applications, aimed at helping those who need to model structured dependence between dynamic random phenomena.

ISBN: 9781316650530$q(electronic bk.)Subjects--Topical Terms:

181910
Markov processes.

LC Class. No.: QA274.7 / .B55 2020

Dewey Class. No.: 519.233

Structured dependence between stochastic processes
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100 1 $a Bielecki, Tomasz R. $3 981476
245 1 0 $a Structured dependence between stochastic processes $h [electronic resource] / $c Tomasz R. Bielecki, Jacek Jakubowski, Mariusz Niewęgłowski
260 $a Cambridge : $b Cambridge University Press, $c 2020.
300 $a viii, 269 p. : $b ill., digital ; $c 25 cm.
490 1 $a Encyclopedia of mathematics and its applications ; $v 175
500 $a Title from publisher's bibliographic system (viewed on 21 Sep 2020).
505 0 $a Strong Markov consistency of multivariate Markov families and processes -- Consistency of finite multivariate Markov chains -- Consistency of finite multivariate conditional Markov chains -- Consistency of multivariate special semimartingales -- Strong Markov family structures -- Markov chain structures -- Conditional Markov chain structures -- Special semimartingale structures -- Archimedean survival processes, Markov consistency, ASP structures -- Generalized multivariate Hawkes processes -- Applications of stochastic structures.
520 $a The relatively young theory of structured dependence between stochastic processes has many real-life applications in areas including finance, insurance, seismology, neuroscience, and genetics. With this monograph, the first to be devoted to the modeling of structured dependence between random processes, the authors not only meet the demand for a solid theoretical account but also develop a stochastic processes counterpart of the classical copula theory that exists for finite-dimensional random variables. Presenting both the technical aspects and the applications of the theory, this is a valuable reference for researchers and practitioners in the field, as well as for graduate students in pure and applied mathematics programs. Numerous theoretical examples are included, alongside examples of both current and potential applications, aimed at helping those who need to model structured dependence between dynamic random phenomena.
650 0 $a Markov processes. $3 181910
650 0 $a Dependence (Statistics) $3 273486
700 1 $a Jakubowski, Jacek. $3 885404
700 1 $a Niewęgłowski, Mariusz. $3 981477
830 0 $a Encyclopedia of mathematics and its applications ; $v v. 124-125. $3 403662
856 4 0 $u https://doi.org/10.1017/9781316650530