| 000 | 02511nam a22003738i 4500 | ||
|---|---|---|---|
| 001 | CR9781139029834 | ||
| 003 | UkCbUP | ||
| 005 | 20240909191119.0 | ||
| 006 | m|||||o||d|||||||| | ||
| 007 | cr|||||||||||| | ||
| 008 | 110221s2016||||enk o ||1 0|eng|d | ||
| 020 | _a9781139029834 (ebook) | ||
| 020 | _z9780521878265 (hardback) | ||
| 040 |
_aUkCbUP _beng _erda _cUkCbUP |
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| 050 | 0 | 0 |
_aQA278.8 _b.G46 2016 |
| 082 | 0 | 0 |
_a519.5/42 _223 |
| 100 | 1 |
_aGhosal, Subhashis, _eauthor. |
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| 245 | 1 | 0 |
_aFundamentals of nonparametric Bayesian inference / _cSubhashis Ghosal, North Carolina State University, Aad van der Vaart, Leiden University. |
| 264 | 1 |
_aCambridge : _bCambridge University Press, _c2016. |
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| 300 |
_a1 online resource (xxiv, 646 pages) : _bdigital, PDF file(s). |
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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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| 490 | 1 |
_aCambridge series in statistical and probabilistic mathematics ; _v44 |
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| 500 | _aTitle from publisher's bibliographic system (viewed on 11 Aug 2017). | ||
| 520 | _aExplosive growth in computing power has made Bayesian methods for infinite-dimensional models - Bayesian nonparametrics - a nearly universal framework for inference, finding practical use in numerous subject areas. Written by leading researchers, this authoritative text draws on theoretical advances of the past twenty years to synthesize all aspects of Bayesian nonparametrics, from prior construction to computation and large sample behavior of posteriors. Because understanding the behavior of posteriors is critical to selecting priors that work, the large sample theory is developed systematically, illustrated by various examples of model and prior combinations. Precise sufficient conditions are given, with complete proofs, that ensure desirable posterior properties and behavior. Each chapter ends with historical notes and numerous exercises to deepen and consolidate the reader's understanding, making the book valuable for both graduate students and researchers in statistics and machine learning, as well as in application areas such as econometrics and biostatistics. | ||
| 650 | 0 | _aNonparametric statistics. | |
| 650 | 0 | _aBayesian statistical decision theory. | |
| 700 | 1 |
_aVaart, A. W. van der, _eauthor. |
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| 776 | 0 | 8 |
_iPrint version: _z9780521878265 |
| 830 | 0 |
_aCambridge series in statistical and probabilistic mathematics ; _v44. |
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| 856 | 4 | 0 | _uhttps://doi.org/10.1017/9781139029834 |
| 942 |
_2ddc _cEB |
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| 999 |
_c9997 _d9997 |
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