This text provides an introduction to the modern theory of economic dynamics, with emphasis on mathematical and computational techniques for modeling dynamic systems. Written to be both rigorous and engaging, the book shows how sound understanding of the underlying theory leads to effective algorithms for solving real world problems. The material makes extensive use of programming examples to illustrate ideas. These programs help bring to life the abstract concepts in the text.
This text offers an introduction to quantum computing, with a special emphasis on basic quantum physics, experiment, and quantum devices. Unlike many other texts, which tend to emphasize algorithms, Quantum Computing without Magic explains the requisite quantum physics in some depth, and then explains the devices themselves.
Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems. Statistical relational learning builds on ideas from probability theory and statistics to address uncertainty while incorporating tools from logic, databases, and programming languages to represent structure.
Machine learning develops intelligent computer systems that are able to generalize from previously seen examples. A new domain of machine learning, in which the prediction must satisfy the additional constraints found in structured data, poses one of machine learning’s greatest challenges: learning functional dependencies between arbitrary input and output domains. This volume presents and analyzes the state of the art in machine learning algorithms and theory in this novel field.
The minimum description length (MDL) principle is a powerful method of inductive inference, the basis of statistical modeling, pattern recognition, and machine learning. It holds that the best explanation, given a limited set of observed data, is the one that permits the greatest compression of the data. MDL methods are particularly well-suited for dealing with model selection, prediction, and estimation problems in situations where the models under consideration can be arbitrarily complex, and overfitting the data is a serious concern.
Investigating meta-programming within the logic programming paradigm, Meta-Logics and Logic Programming presents original research on an important extension of logic programming that makes it more amenable for knowledge representation and programming in general. The 12 contributions, many written especially for this book, explore the foundations, language design issues, and applications of meta-programming in logic programming.
"Consider for a moment the layers of structure and meaning that are attached to concepts like lawsuit, birthday party, fire, mother, walrus, cabbage, or king.... If I tell you that a house burned down, and that the fire started at a child's birthday party, you will think immediately of the candles on the cake and perhaps of the many paper decorations. You will not, In all probability, find yourself thinking about playing pin-the-tail-on-the-donkey or about the color of the cake's icing or about the fact that birthdays come once a year.
Although state variable concepts are a part of modern control theory, they have not been extensively applied in communication theory. The purpose of this book is to demonstrate how the concepts and methods of state variables can be used advantageously in analyzing a variety of communication theory problems.
Randomization is an important tool in the design of algorithms, and the ability of randomization to provide enhanced power is a major research topic in complexity theory. Noam Nisan continues the investigation into the power of randomization and the relationships between randomized and deterministic complexity classes by pursuing the idea of emulating randomness, or pseudorandom generation.
There is increasing interest in genetic programming by both researchers and professional software developers. These twenty-two invited contributions show how a wide variety of problems across disciplines can be solved using this new paradigm.