Bayesian nonparametrics (82 résultats)

Hardcover. Etat : Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.

Soft cover. Etat : As New. 1st Edition. This is a fine, as new, first edition, first printing paperback copy, red wraps. 96 pages with index and bibliography.

Hardcover. Etat : VG-. No Jacket. used hardcover copy, lacking a dust jacket (as issued). light shelfwear, corners perhaps slightly bumped. some discoloration/wear from age. pages and binding are clean, straight, and tight. there are no marks to the text or other serious flaws.

Trade Paperback. Etat : Very Good. Very solid overall, pages tight and unmarked.

Etat : new.

Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : New. 2004. paperback. . . . . .

Etat : New.

Paperback. Etat : Brand New. illustrated edition. 104 pages. 9.50x6.75x0.25 inches. In Stock.

PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000.

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Paperback. Etat : New. Bayesian Nonparametrics via Neural Networks is the first book to focus on neural networks in the context of nonparametric regression and classification, working within the Bayesian paradigm. It discusses neural networks in a statistical context, an approach in contrast to existing books, which tend to treat neural networks as a machine learning algorithm instead of a statistical model. Once this underlying statistical model is recognized, other standard statistical techniques can be applied to improve the model. The Bayesian approach allows better accounting for uncertainty. This book covers uncertainty in model choice and ways to deal with this issue, exploring ideas from statistics and machine learning. An analysis on the choice of prior and new noninformative priors is included, along with a substantial literature review. Written for statisticians using statistical terminology, this book will lead statisticians to an increased understanding of the neural network model and its applicability to real-world problems.…

Hardcover. Etat : Very Good. No Jacket. 1st Edition. Internally clean and unmarked but with some sticker residue to front cover.

Etat : New. 2004. paperback. . . . . . Books ship from the US and Ireland.

Hardcover. Etat : Good+. Hardcover, xii + 305 pages. A thick line in black marker on upper page edges externally. Book is missing the title page (binding has not been damaged); surface sticker-damage inside the front board. Interior is clean and bright with unmarked text, free of inscriptions and stamps, firmly bound. Boards show extensive sunning and faint shelfwear. Issued without a dust jacket.…

Etat : As New. Unread book in perfect condition.

Etat : New. In English.

Kartoniert / Broschiert. Etat : New.

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Etat : New.

Etat : New.

Taschenbuch. Etat : Neu. Neuware - Bayesian Nonparametrics via Neural Networks is the first book to focus on neural networks in the context of nonparametric regression and classification, working within the Bayesian paradigm. Its goal is to demystify neural networks, putting them firmly in a statistical context rather than treating them as a black box. This approach is in contrast to existing books, which tend to treat neural networks as a machine learning algorithm instead of a statistical model. Once this underlying statistical model is recognized, other standard statistical techniques can be applied to improve the model.The Bayesian approach allows better accounting for uncertainty. This book covers uncertainty in model choice and methods to deal with this issue, exploring a number of ideas from statistics and machine learning. A detailed discussion on the choice of prior and new noninformative priors is included, along with a substantial literature review. Written for statisticians using statistical terminology, Bayesian Nonparametrics via Neural Networks will lead statisticians to an increased understanding of the neural network model and its applicability to real-world problems.To illustrate the major mathematical concepts, the author uses two examples throughout the book: one on ozone pollution and the other on credit applications. The methodology demonstrated is relevant for regression and classification-type problems and is of interest because of the widespread potential applications of the methodologies described in the book.…

Etat : New. In English.

Etat : New.

Etat : As New. Unread book in perfect condition.

Hardback. Etat : 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. …

Hardcover. Etat : New. Brand new book, sourced directly from publisher. Dispatch time is 7-8 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Etat : New.

Paperback. Etat : New. Bayesian Nonparametrics via Neural Networks is the first book to focus on neural networks in the context of nonparametric regression and classification, working within the Bayesian paradigm. It discusses neural networks in a statistical context, an approach in contrast to existing books, which tend to treat neural networks as a machine learning algorithm instead of a statistical model. Once this underlying statistical model is recognized, other standard statistical techniques can be applied to improve the model. The Bayesian approach allows better accounting for uncertainty. This book covers uncertainty in model choice and ways to deal with this issue, exploring ideas from statistics and machine learning. An analysis on the choice of prior and new noninformative priors is included, along with a substantial literature review. Written for statisticians using statistical terminology, this book will lead statisticians to an increased understanding of the neural network model and its applicability to real-world problems.…