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Probability theory II = stochastic calculus /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Probability theory II/ by Andrea Pascucci.
其他題名:	stochastic calculus /
作者:	Pascucci, Andrea.
出版者:	Cham :Springer Nature Switzerland : : 2024.,
面頁冊數:	xix, 426 p. :ill. (chiefly col.), digital ;24 cm.
內容註:	1 Stochastic processes -- 2 Markov processes -- 3 Continuous processes -- 4 Brownian motion -- 5 Poisson process -- 6 Stopping times -- 7 Strong Markov property -- 8 Continuous martingales -- 9 Theory of variation -- 10 Stochastic integral -- 11 Itô's formula -- 12 Multidimensional stochastic calculus -- 13 Change of measure and martingale representation -- 14 Stochastic differential equations -- 15 Feynman-Kac formulas -- 16 Linear stochastic equations -- 17 Strong solutions -- 18 Weak solutions -- 19 Complements -- 20 A primer on parabolic PDEs.
Contained By:	Springer Nature eBook
標題:	Probabilities. -
電子資源:	https://doi.org/10.1007/978-3-031-63193-1
ISBN:	9783031631931

Probability theory II = stochastic calculus /
Pascucci, Andrea.

Probability theory IIstochastic calculus /[electronic resource] :by Andrea Pascucci. - Cham :Springer Nature Switzerland :2024. - xix, 426 p. :ill. (chiefly col.), digital ;24 cm. - La matematica per il 3+2,v. 1662038-5757 ;. - La matematica per il 3+2 ;v. 166..

1 Stochastic processes -- 2 Markov processes -- 3 Continuous processes -- 4 Brownian motion -- 5 Poisson process -- 6 Stopping times -- 7 Strong Markov property -- 8 Continuous martingales -- 9 Theory of variation -- 10 Stochastic integral -- 11 Itô's formula -- 12 Multidimensional stochastic calculus -- 13 Change of measure and martingale representation -- 14 Stochastic differential equations -- 15 Feynman-Kac formulas -- 16 Linear stochastic equations -- 17 Strong solutions -- 18 Weak solutions -- 19 Complements -- 20 A primer on parabolic PDEs.

This book offers a modern approach to the theory of continuous-time stochastic processes and stochastic calculus. The content is treated rigorously, comprehensively, and independently. In the first part, the theory of Markov processes and martingales is introduced, with a focus on Brownian motion and the Poisson process. Subsequently, the theory of stochastic integration for continuous semimartingales was developed. A substantial portion is dedicated to stochastic differential equations, the main results of solvability and uniqueness in weak and strong sense, linear stochastic equations, and their relation to deterministic partial differential equations. Each chapter is accompanied by numerous examples. This text stems from over twenty years of teaching experience in stochastic processes and calculus within master's degrees in mathematics, quantitative finance, and postgraduate courses in mathematics for applications and mathematical finance at the University of Bologna. The book provides material for at least two semester-long courses in scientific studies (Mathematics, Physics, Engineering, Statistics, Economics, etc.) and aims to provide a solid background for those interested in the development of stochastic calculus theory and its applications. This text completes the journey started with the first volume of Probability Theory I - Random Variables and Distributions, through a selection of advanced classic topics in stochastic analysis.

ISBN: 9783031631931

Standard No.: 10.1007/978-3-031-63193-1doiSubjects--Topical Terms:

518889
Probabilities.

LC Class. No.: QA273

Dewey Class. No.: 519.2

Probability theory II = stochastic calculus /
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520 $a This book offers a modern approach to the theory of continuous-time stochastic processes and stochastic calculus. The content is treated rigorously, comprehensively, and independently. In the first part, the theory of Markov processes and martingales is introduced, with a focus on Brownian motion and the Poisson process. Subsequently, the theory of stochastic integration for continuous semimartingales was developed. A substantial portion is dedicated to stochastic differential equations, the main results of solvability and uniqueness in weak and strong sense, linear stochastic equations, and their relation to deterministic partial differential equations. Each chapter is accompanied by numerous examples. This text stems from over twenty years of teaching experience in stochastic processes and calculus within master's degrees in mathematics, quantitative finance, and postgraduate courses in mathematics for applications and mathematical finance at the University of Bologna. The book provides material for at least two semester-long courses in scientific studies (Mathematics, Physics, Engineering, Statistics, Economics, etc.) and aims to provide a solid background for those interested in the development of stochastic calculus theory and its applications. This text completes the journey started with the first volume of Probability Theory I - Random Variables and Distributions, through a selection of advanced classic topics in stochastic analysis.
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