Exploring Monte Carlo Methods is a basic text that describes the numerical methods that have come to be known as "Monte Carlo." The book treats the subject generically through the first eight chapters and, thus, should be of use to anyone who wants to learn to use Monte Carlo. The next two chapters focus on applications in nuclear...

Increasing performance demands in integrated circuits, together with limited energy budgets, force IC designers to find new ways of saving power. One innovative way is the presented adaptive voltage scaling scheme, which tunes the supply voltage according to the present process, voltage and temperature variations as well as aging. The voltage...

This volume provides a general overview of discrete- and continuous-time Markov control processes and stochastic games, along with a look at the range of applications of stochastic control and some of its recent theoretical developments. These topics include various aspects of dynamic programming, approximation...

Abstraction is a fundamental mechanism underlying both human and artificial perception, representation of knowledge, reasoning and learning. This mechanism plays a crucial role in many disciplines, notably Computer Programming, Natural and Artificial Vision, Complex Systems, Artificial Intelligence and Machine Learning, Art, and Cognitive...

Risk analytics is developing rapidly, and analysts in the field need material that is theoretically sound as well as practical and straightforward. A one-stop resource for quantitative risk analysis, Practical Spreadsheet Risk Modeling for Management dispenses with the use of complex mathematics, concentrating on how powerful...

Over the past few decades the powerful methods of statistical physics and Euclidean quantum field theory have moved closer together, with common tools based on the use of path integrals. The interpretation of Euclidean field theories as particular systems of statistical physics has opened up new avenues for understanding strongly coupled...

The book presents some of the most efficient statistical and deterministic methods for information processing and applications in order to extract targeted information and find hidden patterns. The techniques presented range from Bayesian approaches and their variations such as sequential Monte Carlo methods, Markov Chain Monte Carlo filters,...

This second edition of the popular textbook contains a comprehensive course in modern probability theory. Overall, probabilistic concepts play an increasingly important role in mathematics, physics, biology, financial engineering and computer science. They help us in understanding magnetism, amorphous media, genetic diversity and the perils...

Bayesian inference provides a simple and unified approach to data analysis, allowing experimenters to assign probabilities to competing hypotheses of interest, on the basis of the current state of knowledge. By incorporating relevant prior information, it can sometimes improve model parameter estimates by many orders of magnitude. This book...

The increasing availability of data in our current, informationoverloaded society has led to the need for valid tools for itsmodelling and analysis. Data mining and applied statistical methodsare the appropriate tools to extract knowledge from such data. Thisbook provides an accessible introduction to data mining methods ina consistent and...

• Design and troubleshoot machine learning systems for common tasks including regression, classification, and clustering
• Acquaint yourself with popular machine learning algorithms, including...