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Word Processing in Groups
Word Processing in Groups

Connections between the theory of hyperbolic manifolds and the theory of automata are deeply interwoven in the history of mathematics of this century.

The use of symbol sequences to study dynamical systems originates in the work of Kocbe [Koc27, Koe29] and Morse [Mor87j, who both used symbol saliences to code geodesies on a...

Anticipatory Optimization for Dynamic Decision Making (Operations Research/Computer Science Interfaces Series)
Anticipatory Optimization for Dynamic Decision Making (Operations Research/Computer Science Interfaces Series)

Anticipatory optimization for dynamic decision making relies on a number of different scientific disciplines. On a general level, the foundations of the field may be localized at the intersection of operations research, computer science and decision theory. Closer inspection reveals the important role of branches such as simulation,...

Understanding Computational Bayesian Statistics (Wiley Series in Computational Statistics)
Understanding Computational Bayesian Statistics (Wiley Series in Computational Statistics)
In theory, Bayesian statistics is very simple. The posterior is proportional to the prior times likelihood. This gives the shape of the posterior, but it is not a density so it cannot be used for inference. The exact scale factor needed to make this a density can be found only in a few special cases. For other cases, the scale...
Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)
Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)

Most tasks require a person or an automated system to reason--to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning...

Computer Vision: Models, Learning, and Inference
Computer Vision: Models, Learning, and Inference

There are already many computer vision textbooks, and it is reasonable to question the need for another. Let me explain why I chose to write this volume.

Computer vision is an engineering discipline; we are primarily motivated by the real-world concern of...

A Second Course in Probability
A Second Course in Probability

The 2006 INFORMS Expository Writing Award-winning and best-selling author Sheldon Ross (University of Southern California) teams up with Erol Peköz (Boston University) to bring you this textbook for undergraduate and graduate students in statistics, mathematics, engineering, finance, and actuarial science. This is a guided tour designed...

Signals and Boundaries: Building Blocks for Complex Adaptive Systems
Signals and Boundaries: Building Blocks for Complex Adaptive Systems

Complex adaptive systems (cas), including ecosystems, governments, biological cells, and markets, are characterized by intricate hierarchical arrangements of boundaries and signals. In ecosystems, for example, niches act as semi-permeable boundaries, and smells and visual patterns serve as signals; governments have departmental hierarchies...

Basic Concepts in Computational Physics
Basic Concepts in Computational Physics

With the development of ever more powerful computers a new branch of physics and engineering evolved over the last few decades: Computer Simulation or Computational Physics. It serves two main purposes:

- Solution of complex mathematical problems such as, differential equations, minimization/optimization, or high-dimensional...

Operations Research: A Model-Based Approach (Springer Texts in Business and Economics)
Operations Research: A Model-Based Approach (Springer Texts in Business and Economics)

The book covers the standard models and techniques used in decision making in organizations. The main emphasis of the book is on modeling business-related scenarios and the generation of decision alternatives. Fully solved examples from many areas are used to illustrate the main concepts without getting bogged down in technical details. The...

Advanced Digital Signal Processing and Noise Reduction
Advanced Digital Signal Processing and Noise Reduction

Digital signal processing plays a central role in the development of modern communication and information processing systems. The theory and application of signal processing is concerned with the identification, modelling and utilisation of patterns and structures in a signal process. The observation signals are often distorted, incomplete...

Approximate Iterative Algorithms
Approximate Iterative Algorithms

Iterative algorithms often rely on approximate evaluation techniques, which may include statistical estimation, computer simulation or functional approximation. This volume presents methods for the study of approximate iterative algorithms, providing tools for the derivation of error bounds and convergence rates, and for the optimal design of...

Data Algorithms: Recipes for Scaling Up with Hadoop and Spark
Data Algorithms: Recipes for Scaling Up with Hadoop and Spark

If you are ready to dive into the MapReduce framework for processing large datasets, this practical book takes you step by step through the algorithms and tools you need to build distributed MapReduce applications with Apache Hadoop or Apache Spark. Each chapter provides a recipe for solving a massive computational problem, such as...

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