Isbn: 9789971508593 - Stochastic Complexity in Statistical Inquiry: 15 (world Scientific Series in Computer Science) (7 results)
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Seller: Anybook.com, Lincoln, United KingdomAnybook.com
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Condition: Good. Volume 15. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. Book contains pencil markings. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,500grams, ISBN:9789971508593.…
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hardcover. Condition: Very Good.
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Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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Hardcover. Condition: Brand New. 188 pages. 8.50x6.50x0.50 inches. In Stock.
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Seller: moluna, Greven, Germanymoluna
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.
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Buch. Condition: Neu. STOCHASTIC COMPLEXITY IN STATIST.(V15) | Rissanen J | Buch | WS SERIES IN COMPUTER SCI | Gebunden | Englisch | 1989 | World Scientific | EAN 9789971508593 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.…
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Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book describes how model selection and statistical inference can be founded on the shortest code length for the observed data, called the stochastic complexity. This generalization of the algorithmic complexity not only offers an objective view of statistics, where no prejudiced assumptions of 'true' data generating distributions are needed, but it also in one stroke leads to calculable expressions in a range of situations of practical interest and links very closely with mainstream statistical theory. The search for the smallest stochastic complexity extends the classical maximum likelihood technique to a new global one, in which models can be compared regardless of their numbers of parameters. The result is a natural and far reaching extension of the traditional theory of estimation, where the Fisher information is replaced by the stochastic complexity and the Cramer-Rao inequality by an extension of the Shannon-Kullback inequality. Ideas are illustrated with applications from parametric and non-parametric regression, density and spectrum estimation, time series, hypothesis testing, contingency tables, and data compression.…
