Isbn: 9780521857000 - algebraic statistics for computational biology (7 Ergebnisse)

Zustand: Very Good. Item in very good condition! Textbooks may not include supplemental items i.e. CDs, access codes etc.

Zustand: Good. Good condition ex-library book with usual library markings and stickers.

Zustand: Fine. 420 pp., hardcover, a tiny bit of age-toning to top edge of pages else fine. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country.…

Zustand: New. In English.

Zustand: New. This book, first published in 2005, offers an introduction to the application of algebraic statistics to computational biology. Editor(s): Pachter, L. (University of California, Berkeley); Sturmfels, Bernd. Num Pages: 434 pages, 100 b/w illus. 3 colour illus. 5 tables. BIC Classification: PS. Category: (P) Professional & Vocational. Dimension: 262 x 185 x 29. Weight in Grams: 1062. . 2005. Illustrated. hardcover. . . . . Books ship from the US and Ireland.…

Hardcover. Zustand: Brand New. new title edition. 420 pages. 10.00x7.00x1.00 inches. In Stock.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - The quantitative analysis of biological sequence data is based on methods from statistics coupled with efficient algorithms from computer science. Algebra provides a framework for unifying many of the seemingly disparate techniques used by computational biologists. This book, first published in 2005, offers an introduction to this mathematical framework and describes tools from computational algebra for designing new algorithms for exact, accurate results. These algorithms can be applied to biological problems such as aligning genomes, finding genes and constructing phylogenies. The first part of this book consists of four chapters on the themes of Statistics, Computation, Algebra and Biology, offering speedy, self-contained introductions to the emerging field of algebraic statistics and its applications to genomics. In the second part, the four themes are combined and developed to tackle real problems in computational genomics. As the first book in the exciting and dynamic area, it will be welcomed as a text for self-study or for advanced undergraduate and beginning graduate courses.…