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Published by SIAM: Society for Industrial and Applied Mathematics, 2004
ISBN 10: 0898715636 ISBN 13: 9780898715637
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Published by Cambridge University Press, 2010
ISBN 10: 0521513464 ISBN 13: 9780521513463
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Published by Society for Industrial and Applied Mathematics, 2004
ISBN 10: 0898715636 ISBN 13: 9780898715637
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Language: English
Published by Society for Industrial & Applied, 2004
ISBN 10: 0898715636 ISBN 13: 9780898715637
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Published by Society for Industrial and Applied Mathematics (SIAM), 2004
ISBN 10: 0898715636 ISBN 13: 9780898715637
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Published by Society for Industrial and Applied Mathematics, 2004
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Language: English
Published by Cambridge University Press, 2010
ISBN 10: 0521513464 ISBN 13: 9780521513463
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Published by Cambridge University Press, 2010
ISBN 10: 0521513464 ISBN 13: 9780521513463
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Language: English
Published by Cambridge University Press, 2010
ISBN 10: 0521513464 ISBN 13: 9780521513463
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Published by Cambridge University Press, 2010
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ISBN 10: 0521513464 ISBN 13: 9780521513463
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Add to basketHardback. Condition: New. Bayesian nonparametrics works - theoretically, computationally. The theory provides highly flexible models whose complexity grows appropriately with the amount of data. Computational issues, though challenging, are no longer intractable. All that is needed is an entry point: this intelligent book is the perfect guide to what can seem a forbidding landscape. Tutorial chapters by Ghosal, Lijoi and Prünster, Teh and Jordan, and Dunson advance from theory, to basic models and hierarchical modeling, to applications and implementation, particularly in computer science and biostatistics. These are complemented by companion chapters by the editors and Griffin and Quintana, providing additional models, examining computational issues, identifying future growth areas, and giving links to related topics. This coherent text gives ready access both to underlying principles and to state-of-the-art practice. Specific examples are drawn from information retrieval, NLP, machine vision, computational biology, biostatistics, and bioinformatics.
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Published by Cambridge University Press, 2010
ISBN 10: 0521513464 ISBN 13: 9780521513463
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Published by Chapman and Hall/CRC, 2023
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Published by Chapman and Hall/CRC, 2023
ISBN 10: 036734100X ISBN 13: 9780367341008
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Published by Chapman and Hall/CRC, 2023
ISBN 10: 036734100X ISBN 13: 9780367341008
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Published by Springer, New York, &c., 2003
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Published by Chapman and Hall/CRC, 2023
ISBN 10: 036734100X ISBN 13: 9780367341008
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Published by Taylor & Francis Ltd, 2023
ISBN 10: 036734100X ISBN 13: 9780367341008
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Language: English
Published by Chapman and Hall/CRC, 2023
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Published by Chapman and Hall/CRC, 2023
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Condition: Sehr gut. Zustand: Sehr gut | Seiten: 324 | Sprache: Englisch | Produktart: Bücher | Bayesian nonparametrics has grown tremendously in the last three decades, especially in the last few years. This book is the first systematic treatment of Bayesian nonparametric methods and the theory behind them. While the book is of special interest to Bayesians, it will also appeal to statisticians in general because Bayesian nonparametrics offers a whole continuous spectrum of robust alternatives to purely parametric and purely nonparametric methods of classical statistics. The book is primarily aimed at graduate students and can be used as the text for a graduate course in Bayesian nonparametrics. Though the emphasis of the book is on nonparametrics, there is a substantial chapter on asymptotics of classical Bayesian parametric models. Jayanta Ghosh has been Director and Jawaharlal Nehru Professor at the Indian Statistical Institute and President of the International Statistical Institute. He is currently professor of statistics at Purdue University. He has been editor of Sankhya and served on the editorial boards of several journals including the Annals of Statistics. Apart from Bayesian analysis, his interests include asymptotics, stochastic modeling, high dimensional model selection, reliability and survival analysis and bioinformatics. R.V. Ramamoorthi is professor at the Department of Statistics and Probability at Michigan State University. He has published papers in the areas of sufficiency invariance, comparison of experiments, nonparametric survival analysis and Bayesian analysis. In addition to Bayesian nonparametrics, he is currently interested in Bayesian networks and graphical models. He is on the editorial board of Sankhya.
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