000 | 03035nam a22005055i 4500 | ||
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001 | 978-1-4614-7218-6 | ||
003 | DE-He213 | ||
005 | 20140220082828.0 | ||
007 | cr nn 008mamaa | ||
008 | 130611s2013 xxu| s |||| 0|eng d | ||
020 |
_a9781461472186 _9978-1-4614-7218-6 |
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024 | 7 |
_a10.1007/978-1-4614-7218-6 _2doi |
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050 | 4 | _aQC174.7-175.36 | |
072 | 7 |
_aPHS _2bicssc |
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072 | 7 |
_aPHDT _2bicssc |
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072 | 7 |
_aSCI055000 _2bisacsh |
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082 | 0 | 4 |
_a621 _223 |
100 | 1 |
_aAbarbanel, Henry. _eauthor. |
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245 | 1 | 0 |
_aPredicting the Future _h[electronic resource] : _bCompleting Models of Observed Complex Systems / _cby Henry Abarbanel. |
264 | 1 |
_aNew York, NY : _bSpringer New York : _bImprint: Springer, _c2013. |
|
300 |
_aXVI, 238 p. 97 illus., 91 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aUnderstanding Complex Systems, _x1860-0832 |
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505 | 0 | _aPreface -- 1 An Overview; The Challenge of Complex Systems -- 2 Examples as a Guide to the Issues -- 3 General Formulation of Statistical Data Assimilation -- 4 Evaluating the Path Integral -- 5 Twin Experiments -- 6 Analysis of Experimental Data. | |
520 | _aPredicting the Future: Completing Models of Observed Complex Systems provides a general framework for the discussion of model building and validation across a broad spectrum of disciplines. This is accomplished through the development of an exact path integral for use in transferring information from observations to a model of the observed system. Through many illustrative examples drawn from models in neuroscience, fluid dynamics, geosciences, and nonlinear electrical circuits, the concepts are exemplified in detail. Practical numerical methods for approximate evaluations of the path integral are explored, and their use in designing experiments and determining a model's consistency with observations is investigated. Using highly instructive examples, the problems of data assimilation and the means to treat them are clearly illustrated. This book will be useful for students and practitioners of physics, neuroscience, regulatory networks, meteorology and climate science, network dynamics, fluid dynamics, and other systematic investigations of complex systems. | ||
650 | 0 | _aPhysics. | |
650 | 0 | _aNeurosciences. | |
650 | 0 | _aComputer simulation. | |
650 | 1 | 4 | _aPhysics. |
650 | 2 | 4 | _aStatistical Physics, Dynamical Systems and Complexity. |
650 | 2 | 4 | _aComplex Systems. |
650 | 2 | 4 | _aNumerical and Computational Physics. |
650 | 2 | 4 | _aSimulation and Modeling. |
650 | 2 | 4 | _aNeurosciences. |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9781461472179 |
830 | 0 |
_aUnderstanding Complex Systems, _x1860-0832 |
|
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-1-4614-7218-6 |
912 | _aZDB-2-PHA | ||
999 |
_c95872 _d95872 |