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| 003 | MX-SnUAN | ||
| 005 | 20160429154855.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 150903s2006 xxk| o |||| 0|eng d | ||
| 020 |
_a9781846282546 _99781846282546 |
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| 024 | 7 |
_a10.1007/1846282543 _2doi |
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| 035 | _avtls000343768 | ||
| 039 | 9 |
_a201509030750 _bVLOAD _c201404121001 _dVLOAD _c201404090739 _dVLOAD _y201402061204 _zstaff |
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_aMX-SnUAN _bspa _cMX-SnUAN _erda |
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| 050 | 4 | _aQA75.5-76.95 | |
| 100 | 1 |
_aBöhm, Josef. _eautor _9322680 |
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| 245 | 1 | 0 |
_aOptimized Bayesian Dynamic Advising : _bTheory and Algorithms / _cby Josef Böhm, Tatiana V. Guy, Ladislav Jirsa, Ivan Nagy, Petr Nedoma, Ludvík Tesa? ; edited by Miroslav Kárný. |
| 264 | 1 |
_aLondon : _bSpringer London, _c2006. |
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| 300 |
_axv, 529 páginas 13 ilustraciones _brecurso en línea. |
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| 336 |
_atexto _btxt _2rdacontent |
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| 337 |
_acomputadora _bc _2rdamedia |
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| 338 |
_arecurso en línea _bcr _2rdacarrier |
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| 347 |
_aarchivo de texto _bPDF _2rda |
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| 490 | 0 | _aAdvanced Information and Knowledge Processing | |
| 500 | _aSpringer eBooks | ||
| 505 | 0 | _aUnderlying theory -- Approximate and feasible learning -- Approximate design -- Problem formulation -- Solution and principles of its approximation: learning part -- Solution and principles of its approximation: design part -- Learning with normal factors and components -- Design with normal mixtures -- Learning with Markov-chain factors and components -- Design with Markov-chain mixtures -- Sandwich BMTB for mixture initiation -- Mixed mixtures -- Applications of the advisory system -- Concluding remarks. | |
| 520 | _aWritten by one of the world’s leading groups in the area of Bayesian identification, control and decision making, this book provides the theoretical and algorithmic basis of optimized probabilistic advising. Starting from abstract ideas and formulations, and culminating in detailed algorithms, Optimized Bayesian Dynamic Advising comprises a unified treatment of an important problem of the design of advisory systems supporting supervisors of complex processes. It introduces the theoretical and algorithmic basis of developed advising, relying on novel and powerful combination black-box modeling by dynamic mixture models and fully probabilistic dynamic optimization. The proposed non-standard problem formulation and its solution mark a significant contribution to the design of anthropocentric automation systems. Written for a broad audience, including developers of algorithms and application engineers, researchers, lecturers and postgraduates, this book can be used as a reference tool, and an advanced text on Bayesian dynamic decision making. | ||
| 590 | _aPara consulta fuera de la UANL se requiere clave de acceso remoto. | ||
| 700 | 1 |
_aGuy, Tatiana V. _eautor _9322681 |
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| 700 | 1 |
_aJirsa, Ladislav. _eautor _9322682 |
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| 700 | 1 |
_aNagy, Ivan. _eautor _9322683 |
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| 700 | 1 |
_aNedoma, Petr. _eautor _9322684 |
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| 700 | 1 |
_aTesa?, Ludvík. _eautor _9322685 |
|
| 700 | 1 |
_aKárný, Miroslav. _eeditor. _9322686 |
|
| 710 | 2 |
_aSpringerLink (Servicio en línea) _9299170 |
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| 776 | 0 | 8 |
_iEdición impresa: _z9781852339289 |
| 856 | 4 | 0 |
_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/1-84628-254-3 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL) |
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