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The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource...

Maximum Likelihood Estimation and Inference: With Examples in R, SAS and ADMB
Maximum Likelihood Estimation and Inference: With Examples in R, SAS and ADMB
This book takes a fresh look at the popular and well-established method of maximum likelihood for statistical estimation and inference. It begins with an intuitive introduction to the concepts and background of likelihood, and moves through to the latest developments in maximum likelihood methodology, including general latent variable...
Programming Language Pragmatics, Second Edition
Programming Language Pragmatics, Second Edition
"Michael Scott's book could have been entitled: Why Programming Languages Work. It takes a fresh look at programming languages by bringing together ideas and techniques usually covered in disparate language design, compiler, computer architecture, and operating system courses. Its comprehensive and integrated presentation of language...
Introduction to the Mathematical and Statistical Foundations of Econometrics (Themes in Modern Econometrics)
Introduction to the Mathematical and Statistical Foundations of Econometrics (Themes in Modern Econometrics)

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...

Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods
Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods

This book delivers theoretical and practical knowledge for developing algorithms that infer linear and non-linear multivariate models, providing a methodology for inductive learning of polynomial neural network models (PNN) from data. The text emphasizes an organized model identification process by which to discover models that generalize and...

Artificial Intelligence: Modern Approach
Artificial Intelligence: Modern Approach
Integrates state-of-the-art AI techniques into intelligent agent designs, using examples and exercises to lead the reader from simple reactive agents to full knowledge-based agents with natural language capabilities. Covers areas that are sometimes under-emphasized--reasoning under uncertainty, learning, natural language, vision and robotics--and...
A Computational Model of Natural Language Communication: Interpretation, Inference, and Production in Database Semantics
A Computational Model of Natural Language Communication: Interpretation, Inference, and Production in Database Semantics
Everyday life would be easier if we could simply talk with machines instead of having to program them. Before such talking robots can be built, however, there must be a theory of how communicating with natural language works. This requires not only a grammatical analysis of the language signs, but also a model of the cognitive agent, with...
Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering (Water Science and Technology Library)
Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering (Water Science and Technology Library)

“Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering” provides a systematic account of major concepts and methodologies for data-driven models and presents a unified framework that makes the subject more accessible to and applicable for researchers and practitioners. It integrates important...

Probability and Statistics for Computer Scientists
Probability and Statistics for Computer Scientists

Student-Friendly Coverage of Probability, Statistical Methods, Simulation, and Modeling Tools
Incorporating feedback from instructors and researchers who used the previous edition, Probability and Statistics for Computer Scientists, Second Edition helps students understand general methods of stochastic
...

5 Steps to a 5 AP Statistics, 2010-2011 Edition (5 Steps to a 5 on the Advanced Placement Examinations Series)
5 Steps to a 5 AP Statistics, 2010-2011 Edition (5 Steps to a 5 on the Advanced Placement Examinations Series)

A Perfect Plan for the Perfect Score

We want you to succeed on your AP* exam. That's why we've created this 5-step plan to help you study more effectively, use your preparation time wisely, and get your best score. This easy-to-follow guide offers you a complete review of your AP course, strategies to give you the edge on test...

Mathematical Models of Spoken Language
Mathematical Models of Spoken Language
Humans use language to convey meaningful messages to each other. Linguistic competence consists in the ability to express meaning reliably, not simply to obtain faithful lexical transcriptions. This invaluable reference tool is the product of many years' experience and research on language and speech technology. It presents the motivations for,...
Information Security : Principles and Practice
Information Security : Principles and Practice
Your expert guide to information security

As businesses and consumers become more dependent on complex multinational information systems, the need to understand and devise sound information security systems has never been greater. This title takes a practical approach to information security by focusing on real-world examples. While not...

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