Normal view MARC view ISBD view

Applied stochastic differential equations

By: Sarkka, Simo.
Contributor(s): Solin, Arno [Co author].
Material type: materialTypeLabelBookSeries: Institute of mathematical statistics textbooks. Publisher: New York Cambridge University Press 2019Description: ix, 316 p. Includes index.ISBN: 9781316649466.Subject(s): Stochastic differential equations | Medical technology | Filtering theory | Smoothing theoryDDC classification: 315.350151923 Summary: 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. Contains worked examples and numerical simulation studies in each chapter which make ideas concrete Includes downloadable MATLAB®/Octave source code to support application and adaptation The gentle learning curve focuses on understanding and use rather than technical details https://www.cambridge.org/gb/academic/subjects/statistics-probability/applied-probability-and-stochastic-networks/applied-stochastic-differential-equations?format=PB
Tags from this library: No tags from this library for this title. Log in to add tags.
    average rating: 0.0 (0 votes)
Item type Current location Item location Collection Call number Status Date due Barcode
Books Vikram Sarabhai Library
General Stacks
Slot 435 (0 Floor, West Wing) Non-fiction 315.350151923 S2A7 (Browse shelf) Available 200147

Table of Contents
1. 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.

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.

Contains worked examples and numerical simulation studies in each chapter which make ideas concrete
Includes downloadable MATLAB®/Octave source code to support application and adaptation
The gentle learning curve focuses on understanding and use rather than technical details

https://www.cambridge.org/gb/academic/subjects/statistics-probability/applied-probability-and-stochastic-networks/applied-stochastic-differential-equations?format=PB

There are no comments for this item.

Log in to your account to post a comment.

Powered by Koha