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Applied stochastic differential equations / Simo Sarkka and Arno Solin

By: Contributor(s): Material type: TextTextPublication details: Cambridge, United Kingdom : Cambridge University Press, c2019Description: ix, 316 pages : illustrations ; 23 cmISBN:
  • 9781316649466
Subject(s): LOC classification:
  • QA 274.23 .S27 2019
Contents:
Some background on ordinary differential equations -- Pragmatic introduction to stochastic differential equations -- It├┤ 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.
Item type: Books
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Holdings
Item type Current library Home library Collection Call number Copy number Status Date due Barcode
Books Books National University - Manila LRC - Main General Circulation Gen. Ed. - COE GC QA 274.23 .S27 2019 c.1 (Browse shelf(Opens below)) c.1 Available NULIB000018919

Includes bibliographical references and index.

Some background on ordinary differential equations -- Pragmatic introduction to stochastic differential equations -- It├┤ 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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