Handbook of Geometric Analysis, No. 3 (volume 14 of the Advanced Lectures in Mathematics series)

Geometric Analysis combines differential equations and differential geometry. An important aspect is to solve geometric problems by studying differential equations. Besides some known linear differential operators such as the Laplace operator, many differential equations arising from differential geometry are nonlinear. A particularly important example is the Monge-Ampère equation. Applications to geometric problems have also motivated new methods and techniques in differential equations.

Geometric Analysis combines differential equations with differential geometry. An important aspect of geometric analysis is to approach geometric problems by studying differential equations. Besides some known linear differential operators such as the Laplace operator, many differential equations arising from differential geometry are nonlinear. A particularly important example is the Monge-Amperè equation. Applications to geometric problems have also motivated new methods and techniques in differential equations. The field of geometric analysis is broad and has had many striking applications.

Market Data Explained: A Practical Guide to Global Capital Markets Information

This book is intended to provide a guide to the universe of data content produced by the global capital markets on a daily basis. Commonly referred to as "market data", the universe of content is very wide and the type of information correspondingly diverse. Jargon and acronyms are very common. As a result, users of marker data typically face difficulty in applying the content in analysis and business applications. 

Geometric Partial Differential Equations and Image Analysis

This book provides an introduction to the use of geometric partial differential equations in image processing and computer vision. It brings a number of new concepts into the field, providing a very fundamental and formal approach to image processing. State-of-the-art practical results in a large number of real problems are achieved with the techniques described. Applications covered include image segmentation, shape analysis, image enhancement, and tracking.

Using R for Data Management, Statistical Analysis, and Graphics

Quick and Easy Access to Key Elements of Documentation. Includes worked examples across a wide variety of applications, tasks, and graphics.
Using R for Data Management, Statistical Analysis, and Graphics presents an easy way to learn how to perform an analytical task in R, without having to navigate through the extensive, idiosyncratic, and sometimes unwieldy software documentation and vast number of add-on packages.