9780387763699 - Monte Carlo Strategies in Scientific Computing (Springer Series in Statistics) von Liu, Jun S. (7 Ergebnisse)

Paperback. Zustand: Good. First Edition. It's a preowned item in good condition and includes all the pages. It may have some general signs of wear and tear, such as markings, highlighting, slight damage to the cover, minimal wear to the binding, etc., but they will not affect the overall reading experience.

Zustand: Very Good. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Zustand: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has soft covers. 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,600grams, ISBN:9780387763699.

Zustand: Very Good. Very Good+; Softcover; Very light wear to the covers; Unblemished textblock edges; The endpapers and all text pages are bright and unmarked; The binding is tight with a straight spine; This book will be shipped in a sturdy cardboard box with foam padding; Medium Format (8.5" - 9.75" tall); Tan covers with title in white lettering; 2008, Springer-Verlag Publishing; 360 pages; "Monte Carlo Strategies in Scientific Computing (Springer Series in Statistics)," by Jun S. Liu.

Paperback. Zustand: Gut. 364 pp. Cover with very slight edgewear and slightly discolored at spine, otherwise well preserved copy 326 Sprache: Englisch Gewicht in Gramm: 599.

Kartoniert / Broschiert. Zustand: New. The author is a leading researcher in a very active area of researchEmphasis is on making these methods accessible to scientists who want to apply themIncludes examples from artificial intelligence, computational biology, computer vision an.

Taschenbuch. Zustand: Neu. Neuware - A large number of scientists and engineers use Monte Carlo simulation as an essential tool in their work. This paperback edition (a reprint of the 2001 Springer edition) provides an up-to-date and self-contained summary of recent research results. Given the interdisciplinary nature of the topics and a moderate prerequisite for the reader, this book should be of interest to a broad audience of quantitative researchers such as computational biologists, computer scientists, econometricians, engineers, probabilists, and statisticians. It can also be used as a textbook for a graduate-level course on Monte Carlo methods. Many problems discussed in the later chapters can be potential thesis topics for masters' or Ph.D. students in statistics or computer science departments.