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Markov Random Fields for Vision and Image Processing (MIT Press)
Markov Random Fields for Vision and Image Processing (MIT Press)

This volume demonstrates the power of the Markov random field (MRF) in vision, treating the MRF both as a tool for modeling image data and, utilizing recently developed algorithms, as a means of making inferences about images. These inferences concern underlying image and scene structure as well as solutions to such problems as image...

The Theory of Gambling and Statistical Logic, Second Edition
The Theory of Gambling and Statistical Logic, Second Edition

Early in his rise to enlightenment, man invented a concept that has since been variously viewed as a vice, a crime, a business, a pleasure, a type of magic, a disease, a folly, a weakness, a form of sexual substitution, an expression of the human instinct. He invented gambling.

Recent advances in the field, particularly...

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...
Intelligent Data Analysis
Intelligent Data Analysis
This monograph is a detailed introductory presentation of the key classes of intelligent data analysis methods. The ten coherently written chapters by leading experts provide complete coverage of the core issues.

The first half of the book is devoted to the discussion of classical statistical issues, ranging from the basic concepts of...

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...

Advances in Mathematical Modeling for Reliability
Advances in Mathematical Modeling for Reliability
Advances in Mathematical Modeling for Reliability discusses fundamental issues on mathematical modeling in reliability theory and its applications. Beginning with an extensive discussion of graphical modeling and Bayesian networks, the focus shifts towards repairable systems: a discussion about how sensitive availability calculations parameter...
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...

Privacy-Preserving Data Mining: Models and Algorithms (Advances in Database Systems)
Privacy-Preserving Data Mining: Models and Algorithms (Advances in Database Systems)
Advances in hardware technology have increased the capability to store and record personal data about consumers and individuals, causing concerns that personal data may be used for a variety of intrusive or malicious purposes.

Privacy-Preserving Data Mining: Models and Algorithms proposes a number of techniques to perform the...

The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation (Springer Texts in Statistics)
The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation (Springer Texts in Statistics)

The main purpose of statistical theory is to derive from observations of a random phenomenon an inference about the probability distribution underlying this phenomenon. That is, it provides either an analysis (description) of a past phenomenon, or some predictions about a future phenomenon of a similar nature. In this book, we insist...

Mastering Probabilistic Graphical Models using Python
Mastering Probabilistic Graphical Models using Python

Master probabilistic graphical models by learning through real-world problems and illustrative code examples in Python

About This Book

  • Gain in-depth knowledge of Probabilistic Graphical Models
  • Model time-series problems using Dynamic Bayesian Networks
  • A practical guide to...
From Logic to Logic Programming (Foundations of Computing)
From Logic to Logic Programming (Foundations of Computing)

This mathematically oriented introduction to the theory of logic programming presents a systematic exposition of the resolution method for propositional, first-order, and Horn- clause logics, together with an analysis of the semantic aspects of the method. It is through the inference rule of resolution that both proofs and computations can be...

Why: A Guide to Finding and Using Causes
Why: A Guide to Finding and Using Causes

Can drinking coffee help people live longer? What makes a stock’s price go up? Why did you get the flu? Causal questions like these arise on a regular basis, but most people likely have not thought deeply about how to answer them.

This book helps you think about causality in a structured way: What is a cause, what...

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