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Institute of Mathematical Statistics Textbooks Ser.: Applied Stochastic Differential Equations by Arno Solin and Simo Särkkä (2019, Trade Paperback)
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Applied Stochastic Differential Equations, Paperback by Sarkka, Simo; Solin, Arno, ISBN 1316649466, ISBN-13 9781316649466, Brand New, Free shipping in the US "Stochastic differential equations are differential equations whose solutions are stochastic processes. They exhibit appealing mathematical properties that are useful in modeling uncertainties and noisy phenomena in many disciplines"--
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About this product
Product Identifiers
PublisherCambridge University Press
ISBN-101316649466
ISBN-139781316649466
eBay Product ID (ePID)22038799868
Product Key Features
Number of Pages326 Pages
LanguageEnglish
Publication NameApplied Stochastic Differential Equations
Publication Year2019
SubjectDifferential Equations / General, Probability & Statistics / Stochastic Processes, Probability & Statistics / General
TypeTextbook
AuthorArno Solin, Simo Särkkä
Subject AreaMathematics
SeriesInstitute of Mathematical Statistics Textbooks Ser.
FormatTrade Paperback
Dimensions
Item Height0.7 in
Item Weight16.7 Oz
Item Length9 in
Item Width6 in
Additional Product Features
Intended AudienceScholarly & Professional
LCCN2018-026584
Dewey Edition23
ReviewsAdvance praise: 'Stochastic differential equations have long been used by physicists and engineers, especially in filtering and prediction theory, and more recently have found increasing application in the life sciences, finance and an ever-increasing range of fields. The authors provide intended users with an intuitive, readable introduction and overview without going into technical mathematical details from the often-demanding theory of stochastic analysis, yet clearly pointing out the pitfalls that may arise if its distinctive differences are disregarded. A large part of the book deals with underlying ideas and methods, such as analytical, approximative and computational, which are illustrated through many insightful examples. Linear systems, especially with additive noise and Gaussian solutions, are emphasized, though nonlinear systems are not neglected, and a large number of useful results and formulas are given. The latter part of the book provides an up to date survey and comparison of filtering and parameter estimation methods with many representative algorithms, and culminates with their application to machine learning.' Peter Kloeden, Johann Wolfgang Goethe-Universitt Frankfurt am Main
Series Volume NumberSeries Number 10
IllustratedYes
Dewey Decimal315/.350151923
Table Of Content1. Introduction; 2. Some background on ordinary differential equations; 3. Pragmatic introduction to stochastic differential equations; 4. Ito calculus and stochastic differential equations; 5. Probability distributions and statistics of SDEs; 6. Statistics of linear stochastic differential equations; 7. Useful theorems and formulas for SDEs; 8. Numerical simulation of SDEs; 9. Approximation of nonlinear SDEs; 10. Filtering and smoothing theory; 11. Parameter estimation in SDE models; 12. Stochastic differential equations in machine learning; 13. Epilogue.
SynopsisThis intuitive hands-on text introduces stochastic differential equations (SDEs) as motivated by applications in target tracking and medical technology, and covers their use in methodologies such as filtering, parameter estimation, and machine learning. Examples include applications of SDEs arising in physics and electrical engineering., Stochastic differential equations are differential equations whose solutions are stochastic processes. They exhibit appealing mathematical properties that are useful in modeling uncertainties and noisy phenomena in many disciplines. This book is motivated by applications of stochastic differential equations in target tracking and medical technology and, in particular, their use in methodologies such as filtering, smoothing, parameter estimation, and machine learning. It builds an intuitive hands-on understanding of what stochastic differential equations are all about, but also covers the essentials of Itô calculus, the central theorems in the field, and such approximation schemes as stochastic Runge-Kutta. Greater emphasis is given to solution methods than to analysis of theoretical properties of the equations. The book's practical approach assumes only prior understanding of ordinary differential equations. The numerous worked examples and end-of-chapter exercises include application-driven derivations and computational assignments. MATLAB/Octave source code is available for download, promoting hands-on work with the methods.
LC Classification NumberQA274.23.S23 2019
