Data-driven computational methods: parameter and operator estimations (Record no. 214693)

000 -LEADER
fixed length control field aam a22 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 200223b 2018 ||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781108472470
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 519.2
Item number H2D2
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Harlim, John
9 (RLIN) 394033
245 ## - TITLE STATEMENT
Title Data-driven computational methods: parameter and operator estimations
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc Cambridge University Press
Date of publication, distribution, etc 2018
Place of publication, distribution, etc Cambridge
300 ## - PHYSICAL DESCRIPTION
Extent xi, 158 p.
Other physical details Includes bibliographical references and index
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc Table of Contents<br/><br/>1. Introduction<br/>2. Markov chain Monte Carlo<br/>3. Ensemble Kalman filters<br/>4. Stochastic spectral methods<br/>5. Karhunen–Loève expansion<br/>6. Diffusion forecast<br/>Appendix A. Elementary probability theory<br/>Appendix B. Stochastic processes<br/>Appendix C. Elementary differential geometry<br/>References<br/>Index.<br/><br/>
520 ## - SUMMARY, ETC.
Summary, etc Modern scientific computational methods are undergoing a transformative change; big data and statistical learning methods now have the potential to outperform the classical first-principles modelling paradigm. This book bridges this transition, connecting the theory of probability, stochastic processes, functional analysis, numerical analysis, and differential geometry. It describes two classes of computational methods to leverage data for modelling dynamical systems. The first is concerned with data fitting algorithms to estimate parameters in parametric models that are postulated on the basis of physical or dynamical laws. The second is on operator estimation, which uses the data to nonparametrically approximate the operator generated by the transition function of the underlying dynamical systems. This self-contained book is suitable for graduate studies in applied mathematics, statistics, and engineering. Carefully chosen elementary examples with supplementary MATLAB® codes and appendices covering the relevant prerequisite materials are provided, making it suitable for self-study.<br/>Grants quick access to techniques, but provides a solid theoretical understanding for those who want to go further<br/>Gives an overview of various topics usually scattered across disciplines<br/>Background material is provided in several short appendices.<br/><br/>https://www.cambridge.org/in/academic/subjects/mathematics/computational-science/data-driven-computational-methods-parameter-and-operator-estimations?format=HB
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Mathematical statistics
9 (RLIN) 394034
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Stochastic analysis
9 (RLIN) 394035
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer science
9 (RLIN) 394036
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Stochastic models
9 (RLIN) 394037
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme
Item type Books
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Permanent location Current location Shelving location Date acquired Source of acquisition Cost, normal purchase price Item location Full call number Barcode Date last seen Cost, replacement price Koha item type
          Non-fiction Vikram Sarabhai Library Vikram Sarabhai Library General Stacks 24/02/2020 19 3.00 Slot 1399 (0 Floor, East Wing) 519.2 H2D2 201519 24/02/2020 4869.03 Books

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