Published by Cambridge University Press, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
Language: English
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Published by Cambridge University Press, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
Language: English
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Published by Cambridge University Press, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
Language: English
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Published by Cambridge University Press, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
Language: English
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Published by Cambridge University Press, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
Language: English
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Published by Cambridge University Press, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
Language: English
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Published by Cambridge University Press, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
Language: English
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hardcover. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
Published by Cambridge University Press CUP, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
Language: English
Seller: Books Puddle, New York, NY, U.S.A.
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Published by Cambridge University Press, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
Language: English
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Published by Cambridge University Press, Cambridge, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
Language: English
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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.
Published by Cambridge University Press, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
Language: English
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Published by Cambridge University Press, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
Language: English
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Published by Cambridge University Press, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
Language: English
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Published by Cambridge University Press, Cambridge, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
Language: English
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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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Published by Cambridge University Press, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
Language: English
Seller: AHA-BUCH GmbH, Einbeck, Germany
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Add to basketBuch. 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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Add to basketHardcover. Condition: Brand New. 646 pages. 10.00x7.25x1.75 inches. In Stock.
Published by Cambridge University Press, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
Language: English
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Published by Cambridge University Press, Cambridge, 2017
ISBN 10: 0521878268 ISBN 13: 9780521878265
Language: English
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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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Add to basketHardcover. Condition: Brand New. 646 pages. 10.00x7.25x1.75 inches. In Stock. This item is printed on demand.
Published by Cambridge University Press, 2019
ISBN 10: 0521878268 ISBN 13: 9780521878265
Language: English
Seller: moluna, Greven, Germany
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Add to basketCondition: 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 .