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Author: Sasha Cyganowski
Publisher: Springer Science & Business Media
ISBN: 3642561446
Category : Mathematics
Languages : en
Pages : 323
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Book Description
This is an introduction to probabilistic and statistical concepts necessary to understand the basic ideas and methods of stochastic differential equations. Based on measure theory, which is introduced as smoothly as possible, it provides practical skills in the use of MAPLE in the context of probability and its applications. It offers to graduates and advanced undergraduates an overview and intuitive background for more advanced studies.
Author: Sasha Cyganowski
Publisher: Springer Science & Business Media
ISBN: 3642561446
Category : Mathematics
Languages : en
Pages : 323
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Book Description
This is an introduction to probabilistic and statistical concepts necessary to understand the basic ideas and methods of stochastic differential equations. Based on measure theory, which is introduced as smoothly as possible, it provides practical skills in the use of MAPLE in the context of probability and its applications. It offers to graduates and advanced undergraduates an overview and intuitive background for more advanced studies.
Author: Sasha Cyganowski
Publisher:
ISBN: 9783642561450
Category :
Languages : en
Pages : 332
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Book Description
Author: Henry C. Tuckwell
Publisher: Routledge
ISBN: 1351452959
Category : Mathematics
Languages : en
Pages : 324
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Book Description
This book provides a clear and straightforward introduction to applications of probability theory with examples given in the biological sciences and engineering. The first chapter contains a summary of basic probability theory. Chapters two to five deal with random variables and their applications. Topics covered include geometric probability, estimation of animal and plant populations, reliability theory and computer simulation. Chapter six contains a lucid account of the convergence of sequences of random variables, with emphasis on the central limit theorem and the weak law of numbers. The next four chapters introduce random processes, including random walks and Markov chains illustrated by examples in population genetics and population growth. This edition also includes two chapters which introduce, in a manifestly readable fashion, the topic of stochastic differential equations and their applications.
Author: Tobias Neckel
Publisher: Walter de Gruyter
ISBN: 8376560263
Category : Mathematics
Languages : en
Pages : 650
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Book Description
This book is a holistic and self-contained treatment of the analysis and numerics of random differential equations from a problem-centred point of view. An interdisciplinary approach is applied by considering state-of-the-art concepts of both dynamical systems and scientific computing. The red line pervading this book is the two-fold reduction of a random partial differential equation disturbed by some external force as present in many important applications in science and engineering. First, the random partial differential equation is reduced to a set of random ordinary differential equations in the spirit of the method of lines. These are then further reduced to a family of (deterministic) ordinary differential equations. The monograph will be of benefit, not only to mathematicians, but can also be used for interdisciplinary courses in informatics and engineering.
Author:
Publisher:
ISBN: 9781489932907
Category : Probabilities
Languages : en
Pages : 292
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Book Description
Author: Avner Friedman
Publisher: Academic Press
ISBN: 1483217876
Category : Mathematics
Languages : en
Pages : 248
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Book Description
Stochastic Differential Equations and Applications, Volume 1 covers the development of the basic theory of stochastic differential equation systems. This volume is divided into nine chapters. Chapters 1 to 5 deal with the basic theory of stochastic differential equations, including discussions of the Markov processes, Brownian motion, and the stochastic integral. Chapter 6 examines the connections between solutions of partial differential equations and stochastic differential equations, while Chapter 7 describes the Girsanov’s formula that is useful in the stochastic control theory. Chapters 8 and 9 evaluate the behavior of sample paths of the solution of a stochastic differential system, as time increases to infinity. This book is intended primarily for undergraduate and graduate mathematics students.
Author: James Blowey
Publisher: Springer Science & Business Media
ISBN: 9783540418467
Category : Mathematics
Languages : en
Pages : 336
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Book Description
A compilation of detailed lecture notes on six topics at the forefront of current research in numerical analysis and applied mathematics. Each set of notes presents a self-contained guide to a current research area and has an extensive bibliography. In addition, most of the notes contain detailed proofs of the key results. The notes start from a level suitable for first year graduate students in applied mathematics, mathematical analysis or numerical analysis, and proceed to current research topics. The reader should therefore be able to quickly gain an insight into the important results and techniques in each area without recourse to the large research literature. Current (unsolved) problems are also described and directions for future research is given.
Author: Avner Friedman
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 322
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Book Description
This text develops the theory of systems of stochastic differential equations and presents applications in probability, partial differential equations, and stochastic control problems. Originally published in 2 volumes, it combines a book of basic theory with a book of applications. Familiarity with elementary probability is the sole prerequisite. 1975 edition.
Author: Jaya P. N. Bishwal
Publisher: Springer
ISBN: 3540744487
Category : Mathematics
Languages : en
Pages : 271
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Book Description
Parameter estimation in stochastic differential equations and stochastic partial differential equations is the science, art and technology of modeling complex phenomena. The subject has attracted researchers from several areas of mathematics. This volume presents the estimation of the unknown parameters in the corresponding continuous models based on continuous and discrete observations and examines extensively maximum likelihood, minimum contrast and Bayesian methods.
Author: Kiyosi Ito
Publisher: SIAM
ISBN: 9781611970234
Category : Mathematics
Languages : en
Pages : 79
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Book Description
A systematic, self-contained treatment of the theory of stochastic differential equations in infinite dimensional spaces. Included is a discussion of Schwartz spaces of distributions in relation to probability theory and infinite dimensional stochastic analysis, as well as the random variables and stochastic processes that take values in infinite dimensional spaces.
