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Applied stochastic differential equations / Simo Sarkka and Arno Solin

By: Sarkka, Simo [author]Contributor(s): Solin, Arno [co-author]Material type: Text

TextSeries: Institute of Mathematical Statistics textbooksPublication details: Cambridge, United Kingdom : Cambridge University Press, c2019Description: ix, 316 pages : illustrations ; 23 cmISBN: 9781316649466Subject(s): STOCHASTIC DIFFERENTIAL EQUATIONSLOC classification: QA 274.23 .S27 2019

Contents:

Some background on ordinary differential equations -- Pragmatic introduction to stochastic differential equations -- Itô calculus and stochastic differential equations -- Probability distributions and statistics of SDEs -- Statistics of linear stochastic differential equations -- Useful theorems and formulas for SDEs -- Numerical simulation of SDEs -- Approximation of non-linear SDEs -- Filtering and smoothing theory -- Parameter estimation in SDE models -- Stochastic differential equations in machine learning.

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.

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Item type	Current library	Home library	Collection	Shelving location	Call number	Copy number	Status	Date due	Barcode
Books	LRC - Main	National University - Manila	Gen. Ed. - COE	General Circulation	GC QA 274.23 .S27 2019 (Browse shelf (Opens below))	c.1	Available		NULIB000018919
Books	LRC - Main	National University - Manila	Gen. Ed. - COE	General Circulation	GC QA 274.23 .S27 2019 c.2 (Browse shelf (Opens below))	c.2	Available		NULIB000019046

Includes bibliographical references and index.

Some background on ordinary differential equations --
Pragmatic introduction to stochastic differential equations --
Itô calculus and stochastic differential equations --
Probability distributions and statistics of SDEs --
Statistics of linear stochastic differential equations --
Useful theorems and formulas for SDEs --
Numerical simulation of SDEs --
Approximation of non-linear SDEs --
Filtering and smoothing theory --
Parameter estimation in SDE models --
Stochastic differential equations in machine learning.

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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