Isbn: 9780521548663 - essentials of statistical inference (cambridge series in statistical and probabilistic mathematics, 16, band 16) (6 Ergebnisse)

Paperback. Zustand: Sehr gut. 236 pp. Very well preserved copy 326 Sprache: Englisch Gewicht in Gramm: 428.

Paperback. Zustand: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

Zustand: New. In English.

Zustand: New. 2010. 1st Edition. Paperback. This textbook presents the concepts and results underlying the Bayesian, frequentist, and Fisherian approaches to statistical inference. Series: Cambridge Series in Statistical and Probabilistic Mathematics. Num Pages: 236 pages, 92 exercises. BIC Classification: PBT. Category: (P) Professional & Vocational; (U) Tertiary Education (US: College). Dimension: 253 x 179 x 28. Weight in Grams: 410. Series: Cambridge Series in Statistical and Probabilistic Mathematics. 236 pages, 92 exercises. This 2005 textbook presents the concepts and results underlying the Bayesian, frequentist, and Fisherian approaches to statistical inference. Cateogry: (P) Professional & Vocational; (U) Tertiary Education (US: College). BIC Classification: PBT. Dimension: 253 x 179 x 28. Weight: 438. . . . . . Books ship from the US and Ireland.…

Paperback. Zustand: Brand New. 1st edition. 225 pages. 9.75x6.75x0.75 inches. In Stock.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Aimed at advanced undergraduate and graduate students in mathematics and related disciplines, this book presents the concepts and results underlying the Bayesian, frequentist and Fisherian approaches, with particular emphasis on the contrasts between them. Computational ideas are explained, as well as basic mathematical theory. Written in a lucid and informal style, this concise text provides both basic material on the main approaches to inference, as well as more advanced material on developments in statistical theory, including: material on Bayesian computation, such as MCMC, higher-order likelihood theory, predictive inference, bootstrap methods and conditional inference. It contains numerous extended examples of the application of formal inference techniques to real data, as well as historical commentary on the development of the subject. Throughout, the text concentrates on concepts, rather than mathematical detail, while maintaining appropriate levels of formality. Each chapter ends with a set of accessible problems.…