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| 003 | KWUST | ||
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_a018921686 _2Uk |
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| 020 |
_a9781108472470 _qhardcover |
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| 020 |
_a1108472478 _qhardcover |
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| 020 |
_z9781108615136 _qelectronic book |
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| 035 | _a(OCoLC)on1024086800 | ||
| 040 |
_aYDX _beng _cYDX _erda _dUPM _dEYM _dUKMGB _dOCLCQ _dOCLCF _dYDXIT _dDLC |
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| 042 | _alccopycat | ||
| 050 | 0 | 0 | _aQA276.H365 2018 |
| 082 | 0 | 4 |
_a519.5 _223 |
| 082 | 0 | 4 |
_a519.2 _223 |
| 100 | 1 |
_aHarlim, John, _eauthor. |
|
| 245 | 1 | 0 |
_aData-driven computational methods : _bparameter and operator estimations / _cJohn Harlim, the Pennsylvania State University. |
| 264 | 1 |
_aCambridge, United Kingdom ; _aNew York, NY : _bCambridge University Press, _c2018. |
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| 264 | 4 | _c©2018 | |
| 300 |
_axi, 158 pages : _billustrations (some color) ; _c26 cm |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_aunmediated _bn _2rdamedia |
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| 338 |
_avolume _bnc _2rdacarrier |
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| 504 | _aIncludes bibliographical references (pages 151-155) and index. | ||
| 520 |
_aModern scientific computational methods are undergoing a transformative change; big data and statistical learning methods now have the potential to outperform the classical first-principles modeling 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 modeling 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.-- _cSource other than the Library of Congress. |
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| 650 | 0 | _aMathematical statistics. | |
| 650 | 0 | _aStochastic analysis. | |
| 650 | 0 | _aComputer science. | |
| 650 | 7 |
_aComputer science. _2fast _0(OCoLC)fst00872451 |
|
| 650 | 7 |
_aStochastic analysis. _2fast _0(OCoLC)fst01133499 |
|
| 776 | 0 | 8 |
_iOnline version: _aHarlim, John. _tData-driven computational methods. _dCambridge, UK : Cambridge University Press, 2018 _z9781108615136 |
| 906 |
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| 942 |
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| 999 |
_c2431 _d2431 |
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