Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely...

It is known that quantum computers have great power for searching unsorted databases....

As in many other fields, biomedical engineers benefit from the use of computational intelligence (CI) tools to solve complex and non-linear problems. The benefits could be even greater if there were scientific literature that specifically focused on the biomedical applications of computational intelligence techniques. The first comprehensive...

Now diffused among end-user devices in mobile and wireline networks, the "cloud" is becoming the "fog." This book focuses on the challenges and solutions related to cloud and fog computing for 5G mobile networks and presents novel approaches to the frameworks and schemes that carry out storage, communication, computation,...

• Covers the latest and advanced concepts of Python such as parallel processing with Python 3.6
• Explore the Python language from its basic installation and setup to concepts such as reactive programming and microservices
• Get introduced to the mechanism for rewriting code in a...

• Understand the principles of Bayesian Inference with less mathematical equations
• Learn state-of-the art Machine Learning methods
• Familiarize...

Revised and updated, this second edition of Walter Gautschi's successful Numerical Analysis explores computational methods for problems arising in the areas of classical analysis, approximation theory, and ordinary differential equations, among others. Topics included in the book are presented with a view toward...

Rainbow connections are natural combinatorial measures that are used in applications to secure the transfer of classified information between agencies in communication networks. Rainbow Connections of Graphs covers this new and emerging topic in graph theory and brings together a majority of the results that deal with the...

Embedded System Design: Modeling, Synthesis and Verification introduces a model-based approach to system level design. It presents modeling techniques for both computation and communication at different levels of abstraction, such as specification, transaction level and cycle-accurate level. It discusses synthesis methods for system level...

Nonparametric function estimation with stochastic data, otherwise known as smoothing, has been studied by several generations of statisticians. Assisted by the ample computing power in today's servers, desktops, and laptops, smoothing methods have been finding their ways into everyday data analysis by practitioners. While scores of...

This book constitutes the thoroughly refereed conference proceedings of the 38th International Symposium on Mathematical Foundations of Computer Science, MFCS 2013, held in Klosterneuburg, Austria, in August 2013. The 67 revised full papers presented together with six invited talks were carefully selected from 191 submissions. Topics covered...