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Fractals in Probability and Analysis
Contributor(s): Bishop, Christopher J. (Author), Peres, Yuval (Author)

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ISBN: 1107134110     ISBN-13: 9781107134119
Publisher: Cambridge University Press
OUR PRICE: $76.94  

Binding Type: Hardcover - See All Available Formats & Editions
Published: December 2016
Qty:

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Additional Information
BISAC Categories:
- Mathematics | Probability & Statistics - General
Dewey: 514.742
LCCN: 2016059345
Series: Cambridge Studies in Advanced Mathematics (Hardcover)
Physical Information: 1.11" H x 6.1" W x 9.42" L (1.51 lbs) 412 pages
Features: Bibliography, Index, Price on Product
 
Descriptions, Reviews, Etc.
Publisher Description:
This is a mathematically rigorous introduction to fractals which emphasizes examples and fundamental ideas. Building up from basic techniques of geometric measure theory and probability, central topics such as Hausdorff dimension, self-similar sets and Brownian motion are introduced, as are more specialized topics, including Kakeya sets, capacity, percolation on trees and the traveling salesman theorem. The broad range of techniques presented enables key ideas to be highlighted, without the distraction of excessive technicalities. The authors incorporate some novel proofs which are simpler than those available elsewhere. Where possible, chapters are designed to be read independently so the book can be used to teach a variety of courses, with the clear structure offering students an accessible route into the topic.

Contributor Bio(s): Bishop, Christopher J.: - Christopher J. Bishop is a professor in the Department of Mathematics at Stony Brook University, New York. He has made contributions to the theory of function algebras, Kleinian groups, harmonic measure, conformal and quasiconformal mapping, holomorphic dynamics and computational geometry.Peres, Yuval: - Yuval Peres is a Principal Researcher at Microsoft Research in Redmond, Washington. He is particularly known for his research in topics such as fractals and Hausdorff measures, random walks, Brownian motion, percolation and Markov chain mixing times.
 
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