| 000 | 02867nam a22004695i 4500 | ||
|---|---|---|---|
| 001 | 978-3-319-02642-8 | ||
| 003 | DE-He213 | ||
| 005 | 20190213151711.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 131202s2014 gw | s |||| 0|eng d | ||
| 020 |
_a9783319026428 _9978-3-319-02642-8 |
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| 024 | 7 |
_a10.1007/978-3-319-02642-8 _2doi |
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| 050 | 4 | _aQA273.A1-274.9 | |
| 050 | 4 | _aQA274-274.9 | |
| 072 | 7 |
_aPBT _2bicssc |
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_aMAT029000 _2bisacsh |
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| 072 | 7 |
_aPBT _2thema |
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| 072 | 7 |
_aPBWL _2thema |
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| 082 | 0 | 4 |
_a519.2 _223 |
| 100 | 1 |
_aMajor, Péter. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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| 245 | 1 | 0 |
_aMultiple Wiener-Itô Integrals _h[electronic resource] : _bWith Applications to Limit Theorems / _cby Péter Major. |
| 250 | _a2nd ed. 2014. | ||
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2014. |
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| 300 |
_aXIII, 126 p. 4 illus. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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| 490 | 1 |
_aLecture Notes in Mathematics, _x0075-8434 ; _v849 |
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| 520 | _aThe goal of this Lecture Note is to prove a new type of limit theorems for normalized sums of strongly dependent random variables that play an important role in probability theory or in statistical physics. Here non-linear functionals of stationary Gaussian fields are considered, and it is shown that the theory of Wiener–Itô integrals provides a valuable tool in their study. More precisely, a version of these random integrals is introduced that enables us to combine the technique of random integrals and Fourier analysis. The most important results of this theory are presented together with some non-trivial limit theorems proved with their help. This work is a new, revised version of a previous volume written with the goalof giving a better explanation of some of the details and the motivation behind the proofs. It does not contain essentially new results; it was written to give a better insight to the old ones. In particular, a more detailed explanation of generalized fields is included to show that what is at the first sight a rather formal object is actually a useful tool for carrying out heuristic arguments. | ||
| 650 | 0 | _aDistribution (Probability theory. | |
| 650 | 1 | 4 |
_aProbability Theory and Stochastic Processes. _0http://scigraph.springernature.com/things/product-market-codes/M27004 |
| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer eBooks | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783319026411 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783319026435 |
| 830 | 0 |
_aLecture Notes in Mathematics, _x0075-8434 ; _v849 |
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| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-319-02642-8 |
| 912 | _aZDB-2-SMA | ||
| 912 | _aZDB-2-LNM | ||
| 999 |
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