Language: English
Published by Cambridge University Press (edition 1), 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
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Language: English
Published by Cambridge University Press, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
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Language: English
Published by Cambridge University Press, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
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Language: English
Published by Cambridge University Press, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
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First Edition
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Language: English
Published by Cambridge University Press, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
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Condition: As New. Unread book in perfect condition.
Language: English
Published by Cambridge University Press, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
Seller: GreatBookPrices, Columbia, MD, U.S.A.
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Language: English
Published by Cambridge University Press CUP, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
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Condition: New. pp. 656.
Language: English
Published by Cambridge University Press, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
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Language: English
Published by Cambridge University Press, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
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Language: English
Published by Cambridge University Press, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
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Language: English
Published by Cambridge University Press, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
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Condition: New. pp. 656.
Language: English
Published by Cambridge University Press, Cambridge, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
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Hardcover. Condition: new. Hardcover. 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. 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 biostatistics. Appendices with prerequisites and numerous exercises support its use for graduate courses. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Language: English
Published by Cambridge University Press, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Language: English
Published by Cambridge University Press, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
Seller: Leipziger Antiquariat, Leipzig, Germany
Condition: Gut. 646 Seiten Zustand: Einband etwas berieben, Ecken etwas bestoßen, Schnitt etwas abgegriffen // Text in Englisch. Unser Produktfoto entspricht dem hier angebotenen Artikel. Alle Artikel befinden sich stets in gebrauchsfähigem Zustand. Gebrauchte Bücher sparen Ressourcen gegenüber Neuware und schonen die Umwelt. /// Versand gratis Innerhalb Deutschlands - Portofrei in Deutschland- ab 20 Euro mit Post ID - Gratisversand deutschlandweit innerhalb Deutschlands gratis Versand -Versandkostenfrei innerhalb Deutschlands /// Sprache: Englisch Gewicht in Gramm: 1440 26,0 x 18,5 cm, Pappband/Hardcover.
Language: English
Published by Cambridge University Press, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
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Language: English
Published by Cambridge University Press, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
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Language: English
Published by Cambridge University Press, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
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Add to basketCondition: good. May show signs of wear, highlighting, writing, and previous use. This item may be a former library book with typical markings. No guarantee on products that contain supplements Your satisfaction is 100% guaranteed. Twenty-five year bookseller with shipments to over fifty million happy customers.
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Add to basketHardcover. Condition: Brand New. 646 pages. 10.00x7.25x1.75 inches. In Stock.
Language: English
Published by Cambridge University Press, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
Seller: AHA-BUCH GmbH, Einbeck, Germany
Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Bayesian nonparametrics comes of age with this landmark text synthesizing theory, methodology and computation.
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Language: English
Published by Cambridge University Press, Cambridge, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
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Add to basketHardcover. Condition: new. Hardcover. 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. 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 biostatistics. Appendices with prerequisites and numerous exercises support its use for graduate courses. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Language: English
Published by Cambridge University Press, 2019
ISBN 10: 0521878268 ISBN 13: 9780521878265
Seller: moluna, Greven, Germany
US$ 133.09
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Add to basketGebunden. Condition: 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 .
Language: English
Published by Cambridge University Press, Cambridge, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
Seller: AussieBookSeller, Truganina, VIC, Australia
Hardcover. Condition: new. Hardcover. 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. 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 biostatistics. Appendices with prerequisites and numerous exercises support its use for graduate courses. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Language: English
Published by Cambridge University Press, 2019
ISBN 10: 0521878268 ISBN 13: 9780521878265
Seller: preigu, Osnabrück, Germany
Buch. Condition: Neu. Fundamentals of Nonparametric Bayesian Inference | Subhashis Ghosal (u. a.) | Buch | Gebunden | Englisch | 2019 | Cambridge University Press | EAN 9780521878265 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.