Most duties require a person or an automatic system to motive–to reach conclusions based mostly on accessible information. The framework of probabilistic graphical models, introduced in this book, gives a common approach for this task. The strategy is mannequin-based, permitting interpretable models to be constructed after which manipulated by reasoning algorithms.
These models can also be realized automatically from information, permitting the approach to be used in circumstances where manually constructing a model is troublesome or even impossible. As a result of uncertainty is an inescapable facet of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models which are more faithful to reality.
Probabilistic Graphical Models Principles and Techniques by Daphne Kollerand Nir Friedman discusses variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to take care of dynamical programs and relational data. For every class of models, the text describes the three basic cornerstones: illustration, inference, and learning, presenting each fundamental ideas and superior techniques.
Most chapters also embody boxes with additional material: skill bins, which describe techniques; case study boxes, which debate empirical instances related to the method described within the text, together with applications in computer vision, robotics, natural language understanding, and computational biology; and idea boxes, which current vital ideas drawn from the material within the chapter. Instructors (and readers) can group chapters in varied combinations, from core matters to more technically superior material, to suit their specific needs.
Finally, the book considers using the proposed framework for causal reasoning and resolution making underneath uncertainty. The primary text in every chapter provides the detailed technical development of the important thing ideas.
Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series) [Hardcover]
Daphne Koller and Nir Friedman
The MIT Press; 1 edition (July 31, 2009)
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