Concentration of Measure for the Analysis of Randomized Algorithms

Save on Concentration of Measure for the Analysis of Randomized Algorithms . Massive Saving, Order Now! Want it delivered in Monday ,6 February 2012 ? Choose One-Day Shipping at checkout. Details
Other products by Cambridge University Press Ratting Out of 5.0 Special Offer Total New 15 Use

Rate:
List Price:	$75.00
Our Price:	$53.24
Price Save:	$17.74


Total Price:	$53.24
Usually ships in 24 hours this item ships for FREE with Super Saver Shipping.

at of 2012-02-05 Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on [amazon.com or endless.com, as applicable] at the time of purchase will apply to the purchase of this product.

More Images Product

Buy Low Price From Here Now

Special Offers

Available from 1 Store : Select your deal and buy Concentration of Measure for the Analysis of Randomized Algorithms Where can I buy a Concentration of Measure for the Analysis of Randomized Algorithms ? At all of these merchants listed below. Click any of the deals below to buy now on the merchant's website.

Store	Rating	Prices	Shipping	Link
	New	$57.26	Usually ships in 24 hours

Discription
Customer Reviews
Related

Randomized algorithms have become a central part of the algorithms curriculum based on their increasingly widespread use in modern applications. This book presents a coherent and unified treatment of probabilistic techniques for obtaining high- probability estimates on the performance of randomized algorithms. It covers the basic tool kit from the Chernoff-Hoeffding (CH) bounds to more sophisticated techniques like Martingales and isoperimetric inequalities, as well as some recent developments like Talagrand's inequality, transportation cost inequalities, and log-Sobolev inequalities. Along the way, variations on the basic theme are examined, such as CH bounds in dependent settings. The authors emphasize comparative study of the different methods, highlighting respective strengths and weaknesses in concrete example applications. The exposition is tailored to discrete settings sufficient for the analysis of algorithms, avoiding unnecessary measure-theoretic details, thus making the book accessible to computer scientists as well as probabilists and discrete mathematicians.