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Stochastic integration in Banach spa...

Mandrekar, Vidyadhar.

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Stochastic integration in Banach spaces = theory and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Stochastic integration in Banach spaces/ by Vidyadhar Mandrekar, Barbara Rudiger.
其他題名:	theory and applications /
作者:	Mandrekar, Vidyadhar.
其他作者:	Rudiger, Barbara.
出版者:	Cham :Springer International Publishing : : 2015.,
面頁冊數:	viii, 211 p. :ill., digital ;24 cm.
內容註:	1.Introduction -- 2.Preliminaries -- 3.Stochastic Integrals with Respect to Compensated Poisson Random Measures -- 4.Stochastic Integral Equations in Banach Spaces -- 5.Stochastic Partial Differential Equations in Hilbert Spaces -- 6.Applications -- 7.Stability Theory for Stochastic Semilinear Equations -- A -- Some Results on compensated Poisson random measures and stochastic integrals -- References -- Index.
Contained By:	Springer eBooks
標題:	Banach spaces. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-12853-5
ISBN:	9783319128535 (electronic bk.)

Stochastic integration in Banach spaces = theory and applications /
Mandrekar, Vidyadhar.

Stochastic integration in Banach spacestheory and applications /[electronic resource] :by Vidyadhar Mandrekar, Barbara Rudiger. - Cham :Springer International Publishing :2015. - viii, 211 p. :ill., digital ;24 cm. - Probability theory and stochastic modelling,v.732199-3130 ;. - Probability theory and stochastic modelling ;v.70..

1.Introduction -- 2.Preliminaries -- 3.Stochastic Integrals with Respect to Compensated Poisson Random Measures -- 4.Stochastic Integral Equations in Banach Spaces -- 5.Stochastic Partial Differential Equations in Hilbert Spaces -- 6.Applications -- 7.Stability Theory for Stochastic Semilinear Equations -- A -- Some Results on compensated Poisson random measures and stochastic integrals -- References -- Index.

Considering Poisson random measures as the driving sources for stochastic (partial) differential equations allows us to incorporate jumps and to model sudden, unexpected phenomena. By using such equations the present book introduces a new method for modeling the states of complex systems perturbed by random sources over time, such as interest rates in financial markets or temperature distributions in a specific region. It studies properties of the solutions of the stochastic equations, observing the long-term behavior and the sensitivity of the solutions to changes in the initial data. The authors consider an integration theory of measurable and adapted processes in appropriate Banach spaces as well as the non-Gaussian case, whereas most of the literature only focuses on predictable settings in Hilbert spaces. The book is intended for graduate students and researchers in stochastic (partial) differential equations, mathematical finance and non-linear filtering and assumes a knowledge of the required integration theory, existence and uniqueness results, and stability theory. The results will be of particular interest to natural scientists and the finance community. Readers should ideally be familiar with stochastic processes and probability theory in general, as well as functional analysis, and in particular the theory of operator semigroups.

ISBN: 9783319128535 (electronic bk.)

Standard No.: 10.1007/978-3-319-12853-5doiSubjects--Topical Terms:

579190
Banach spaces.

LC Class. No.: QA322.2

Dewey Class. No.: 515.732

Stochastic integration in Banach spaces = theory and applications /
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245 1 0 $a Stochastic integration in Banach spaces $h [electronic resource] : $b theory and applications / $c by Vidyadhar Mandrekar, Barbara Rudiger.
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505 0 $a 1.Introduction -- 2.Preliminaries -- 3.Stochastic Integrals with Respect to Compensated Poisson Random Measures -- 4.Stochastic Integral Equations in Banach Spaces -- 5.Stochastic Partial Differential Equations in Hilbert Spaces -- 6.Applications -- 7.Stability Theory for Stochastic Semilinear Equations -- A -- Some Results on compensated Poisson random measures and stochastic integrals -- References -- Index.
520 $a Considering Poisson random measures as the driving sources for stochastic (partial) differential equations allows us to incorporate jumps and to model sudden, unexpected phenomena. By using such equations the present book introduces a new method for modeling the states of complex systems perturbed by random sources over time, such as interest rates in financial markets or temperature distributions in a specific region. It studies properties of the solutions of the stochastic equations, observing the long-term behavior and the sensitivity of the solutions to changes in the initial data. The authors consider an integration theory of measurable and adapted processes in appropriate Banach spaces as well as the non-Gaussian case, whereas most of the literature only focuses on predictable settings in Hilbert spaces. The book is intended for graduate students and researchers in stochastic (partial) differential equations, mathematical finance and non-linear filtering and assumes a knowledge of the required integration theory, existence and uniqueness results, and stability theory. The results will be of particular interest to natural scientists and the finance community. Readers should ideally be familiar with stochastic processes and probability theory in general, as well as functional analysis, and in particular the theory of operator semigroups.
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650 2 4 $a Quantitative Finance. $3 891090
650 2 4 $a Partial Differential Equations. $3 890899
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