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The primary goal of this text is to present the theoretical foundation of the field of Fourier analysis. This book is mainly addressed to graduate students in mathematics and is designed to serve for a three-course sequence on the subject. The only prerequisite for understanding the text is satisfactory completion of a course in measure theory, Lebesgue integration, and complex variables. This book is intended to present the selected topics in some depth and stimulate further study.
Data Analysis in Forensic Science: A Bayesian Decision Perspective
This is the first text to examine the use of statistical methods in forensic science and bayesian statistics in combination. The book is split into two parts: Part One concentrates on the philosophies of statistical inference. Chapter One examines the differences between the frequentist, the likelihood and the Bayesian perspectives, before Chapter Two explores the Bayesian decision–theoretic perspective further, and looks at the benefits it carries.
The subject matter of this book is fundamental to all areas of mathematical analysis and should be accessible to students who are in the early stages of their professional training. An undergraduate course in real variables is a prerequisite, and an acquaintance with complex analysis is desirable. However, since no use is made of the Cauchy theory, the exposure to complex variables need not be extensive. To fully appreciate Chapters 3 and 4 some background in elementary point-set topology is essential.
Each issue of BusinessWeek features in-depth perspectives on the financial markets, industries, trends, technology and people guiding the economy. Draw upon Business Week's timely incisive analysis to help you make better decisions about your career, your business, and your personal investments.
