Methods in Analysis Jack Indritz

In this set of lecture notes, we present a culmination of results on infinite matrices which were evolved by the members of the Katowice Br-anch of the Mathematics Institute of the Polish Academy of Sciences. In the early history of functional analysis Hsliding humpH methods were used extensively to establish some of the early abstract results in functional analysis. For example, the first proofs of versions of the Uniform Boundedness Principle by Hahn and Banach and Hildebrand utilized sliding hump methods ([18], [39], [42], [35]).

Direction Dependence in Statistical Modeling: Methods of Analysis incorporates the latest research for the statistical analysis of hypotheses that are compatible with the causal direction of dependence of variable relations. Having particular application in the fields of neuroscience, clinical psychology, developmental psychology, educational psychology, and epidemiology, direction dependence methods have attracted growing attention due to their potential to help decide which of two competing statistical models is more likely to reflect the correct causal flow.

Mathematical models—including those based on ordinary, partial differential, integral, and integro-differential equations—are indispensable tools for studying the physical world and its natural manifestations. Because of the usefulness of these models, it is critical for practitioners to be able to find their solutions by analytic and/or computational means. This two-volume set is a collection of up-to-date research results that illustrate how a very important class of mathematical tools can be manipulated and applied to the study of real-life phenomena and processes occurring in specific problems of science and engineering.

Mathematical models—including those based on ordinary, partial differential, integral, and integro-differential equations—are indispensable tools for studying the physical world and its natural manifestations. Because of the usefulness of these models, it is critical for practitioners to be able to find their solutions by analytic and/or computational means. This two-volume set is a collection of up-to-date research results that illustrate how a very important class of mathematical tools can be manipulated and applied to the study of real-life phenomena and processes occurring in specific problems of science and engineering.

This book provides a comprehensive, self-contained and up-to-date treatment of the main topics in the theory of option pricing. The first part of the text starts with discrete-time models of financial markets, including the Cox-Ross-Rubinstein binomial model.

The Handbook of Mathematical Methods in Imaging provides a comprehensive treatment of the mathematical techniques used in imaging science. The material is grouped into two central themes, namely, Inverse Problems (Algorithmic Reconstruction) and Signal and Image Processing. Each section within the themes covers applications (modeling), mathematics, numerical methods (using a case example) and open questions. Written by experts in the area, the presentation is mathematically rigorous.