This book is intended for use in a rigorous introductory PhD level course in econometrics, or in a field course in econometric theory. It covers the measure-theoretical foundation of probability theory, the multivariate normal distribution with its application to classical linear regression analysis, various laws of large numbers, central limit...

Analyzing computer system performance is often regarded by most system administrators, IT professionals and software engineers as a black art that is too time consuming to learn and apply. Finally, this book by acclaimed performance analyst Dr. Neil Gunther makes this subject understandable and applicable through programmatic examples. The means...

Essentials of Technical Analysis for Financial Markets is an all-encompassing handbook on navigating the financial markets successfully using technical analysis. Clearly written, easy-to-understand, and straightforward, this guide...

Multisensor data fusion is an emerging technology with important applications in both the military and civilian sectors, such as target recognition, robotics, medical diagnostics, and "smart" buildings. It draws on techniques from wide-ranging disciplines, including artificial intelligence, pattern recognition, and statistical...

Fully updated with data from space-based observations and a comprehensive index and bibliography, this third edition contains the most frequently used information in modern astrophysics. As well as a vast number of tables, graphs, diagrams and formulae it also contains information covering topics including atomic physics, nuclear physics,...

Since the publication of the first edition of this seminar book in 1994, the theory and applications of extremes and rare events have enjoyed an enormous and still increasing interest. The intention of the book is to give a mathematically oriented development of the theory of rare events underlying various applications. This characteristic of...

In the first part, this book covers the key concepts of cryptography on...

In the last three decades, machine learning research and practice have focused on batch learning usually using small datasets. In batch learning, the whole training data is available to the algorithm, which outputs a decision model after processing the data eventually (or most of the times) multiple times. The rationale behind this...

The monograph addresses a problem of stochastic analysis based on the uncertainty assessment by simulation and application of this method in ecology and steel industry under uncertainty. The first chapter defines the Monte Carlo (MC) method and random variables in stochastic models. Chapter two deals with the contamination transport in porous...