Graphical Exploratory Data Analysis, S. H. C. DuToit; A. G. W. Steyn; R. H. Stumpf

Автор: Koller Daphne, Friedman Nir
Название: Probabilistic Graphical Models: Principles and Techniques
ISBN: 0262013193 ISBN-13(EAN): 9780262013192
Издательство: MIT Press
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Цена: 141070.00 T
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Описание:

A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions.

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 algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality.

Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.

Автор: Cleff Thomas
Название: Exploratory Data Analysis in Business and Economics
ISBN: 3319015168 ISBN-13(EAN): 9783319015163
Издательство: Springer
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Цена: 65210.00 T
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Описание: Exploratory Data Analysis in Business and Economics

Автор: Wesam Ashour Barbakh; Ying Wu; Colin Fyfe
Название: Non-Standard Parameter Adaptation for Exploratory Data Analysis
ISBN: 3642040047 ISBN-13(EAN): 9783642040047
Издательство: Springer
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Цена: 139750.00 T
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Описание: A review of standard algorithms provides the basis for more complex data mining techniques in this overview of exploratory data analysis. Recent reinforcement learning research is presented, as well as novel methods of parameter adaptation in machine learning.

Автор: C.P.A. Bartels; R.H. Ketellapper
Название: Exploratory and explanatory statistical analysis of spatial data
ISBN: 0898380049 ISBN-13(EAN): 9780898380040
Издательство: Springer
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Цена: 158380.00 T
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Описание: In September 1977 a "Regional Science Symposium" was held at the Faculty of Economics of the University of Goningen in the Netherlands. The first theme, spatial inequalities and regional development, was chosen because of its central place in regional science.

Автор: Roy Frieden; Robert A. Gatenby
Название: Exploratory Data Analysis Using Fisher Information
ISBN: 184996615X ISBN-13(EAN): 9781849966153
Издательство: Springer
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Цена: 135090.00 T
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Описание: This book uses a mathematical approach to deriving the laws of science and technology, based upon the concept of Fisher information. The approach that follows from these ideas is called the principle of Extreme Physical Information (EPI). The authors show how to use EPI to determine the theoretical input/output laws of unknown systems.

Автор: Wesam Ashour Barbakh; Ying Wu; Colin Fyfe
Название: Non-Standard Parameter Adaptation for Exploratory Data Analysis
ISBN: 3642260551 ISBN-13(EAN): 9783642260551
Издательство: Springer
Цена: 139750.00 T
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Описание: Review of Clustering Algorithms.- Review of Linear Projection Methods.- Non-standard Clustering Criteria.- Topographic Mappings and Kernel Clustering.- Online Clustering Algorithms and Reinforcement Learning.- Connectivity Graphs and Clustering with Similarity Functions.- Reinforcement Learning of Projections.- Cross Entropy Methods.- Artificial Immune Systems.- Conclusions.