Verlag: Cambridge University Press, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
Sprache: Englisch
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In den WarenkorbHardcover. Zustand: Like New. First Edition. Hardback book in nearly new condition: firm and square with strong joints. Just a few hardly noticeable rubs; hence a non-text page shows a small 'damaged' stamp. Despite such this book looks and feels unread. Thus the contents are crisp, fresh and tight. And so a very nice book in great condition, now offered for sale at a reasonable price.
Verlag: Cambridge University Press, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
Sprache: Englisch
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Verlag: Cambridge University Press CUP, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
Sprache: Englisch
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Verlag: Cambridge University Press, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
Sprache: Englisch
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In den WarenkorbZustand: New. pp. 656.
Verlag: Cambridge University Press, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
Sprache: Englisch
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Verlag: Cambridge University Press, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
Sprache: Englisch
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Verlag: Cambridge University Press, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
Sprache: Englisch
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Verlag: Cambridge University Press, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
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Verlag: Cambridge University Press, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
Sprache: Englisch
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Verlag: Cambridge University Press, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
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ISBN 10: 0521878268 ISBN 13: 9780521878265
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Verlag: Cambridge University Press, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
Sprache: Englisch
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Verlag: Cambridge University Press, GB, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
Sprache: Englisch
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In den WarenkorbHardback. Zustand: New. Explosive 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.
Verlag: Cambridge University Press, GB, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
Sprache: Englisch
Anbieter: Rarewaves USA United, OSWEGO, IL, USA
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In den WarenkorbHardback. Zustand: New. Explosive 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.
Verlag: Cambridge University Press, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
Sprache: Englisch
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In den WarenkorbHardcover. Zustand: Brand New. 646 pages. 10.00x7.25x1.75 inches. In Stock.
Verlag: Cambridge University Press, GB, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
Sprache: Englisch
Anbieter: Rarewaves.com UK, London, Vereinigtes Königreich
EUR 181,39
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In den WarenkorbHardback. Zustand: New. Explosive 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.
Verlag: Cambridge University Press, GB, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
Sprache: Englisch
Anbieter: Rarewaves.com USA, London, LONDO, Vereinigtes Königreich
EUR 194,07
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In den WarenkorbHardback. Zustand: New. Explosive 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.
Verlag: Cambridge University Press, 2019
ISBN 10: 0521878268 ISBN 13: 9780521878265
Sprache: Englisch
Anbieter: moluna, Greven, Deutschland
EUR 110,34
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In den WarenkorbGebunden. Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Written by top researchers, this self-contained text is the authoritative account of Bayesian nonparametrics, a nearly universal framework for inference in statistics and machine learning, with practical use in all areas of science, including economics and .
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 109,10
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In den WarenkorbHardcover. Zustand: Brand New. 646 pages. 10.00x7.25x1.75 inches. In Stock. This item is printed on demand.