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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...

Theory of Optimal Search
Theory of Optimal Search
This book deals with the problem of optimal allocation of effort to detect a  target. A Bayesian approach is taken in which it is assumed that there is a  prior distribution for the target's location which is known to the searcher as  well as a function which relates the conditional probability of detecting...
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...

Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 11th European Conference
Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 11th European Conference

This book constitutes the refereed proceedings of the 11th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2011, held in Belfast, UK, in June/July 2011. The 60 revised full papers presented together with 3 invited talks were carefully reviewed and selected from 108 submissions. The papers are...

Statistical Bioinformatics: with R
Statistical Bioinformatics: with R

Bioinformatics is an emerging field in which statistical and computational techniques are used extensively to analyze and interpret biological data obtained from high-throughput genomic technologies. Genomic technologies allow us to monitor thousands of biological processes going on inside living organisms in one snapshot, and are...

Think Stats
Think Stats

If you know how to program, you have the skills to turn data into knowledge using the tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.

You'll work with a case study throughout the...

Diagrammatic Reasoning in AI
Diagrammatic Reasoning in AI

This book is really the end product of over a decade of work, on and off, on diagrammatic reasoning in artificial intelligence (AI). In developing this book, I drew inspiration from a variety of sources: two experimental studies, the development of two prototype systems, an extensive literature review and analysis in AI,...

Optimisation in Signal and Image Processing
Optimisation in Signal and Image Processing

This book describes the optimization methods most commonly encountered in signal and image processing: artificial evolution and Parisian approach; wavelets and fractals; information criteria; training and quadratic programming; Bayesian formalism; probabilistic modeling; Markovian approach; hidden Markov models; and metaheuristics (genetic...

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...
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...

Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
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. In...
Modeling with Data: Tools and Techniques for Scientific Computing
Modeling with Data: Tools and Techniques for Scientific Computing
I am a psychiatric geneticist but my degree is in neuroscience, which means that I now do far more statistics than I have been trained for. I cannot overstate to you the magnitude of the change in my productivity since finding this book. Even after reading the first few chapters, which explain why data analysis is painful and how one can implement...
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