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. Get and download textbook Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series) for free
This book presents an exciting new synthesis of directed and undirected, discrete and continuous graphical models. Combining elements of Bayesian networks and Markov random fields, the newly introduced hybrid random fields are an interesting approach to get the best of both these worlds, with an added promise of modularity and scalability. The authors have written an enjoyable book---rigorous in the treatment of the mathematical background, but also enlivened by interesting and original historical and philosophical perspectives.-- Manfred Jaeger, Aalborg UniversitetThe book not only marks an effective direction of investigation with significant experimental advances, but it is also---and perhaps primarily---a guide for the reader through an original trip in the space of probabilistic modeling. While digesting the book, one is enriched with a very open view of the field, with full of stimulating connections. [...] Everyone specifically interested in Bayesian networks and Markov random fields should not miss it.-- Marco Gori, Universita degli Studi di SienaGraphical models are sometimes regarded---incorrectly---as an impractical approach to machine learning, assuming that they only work well for low-dimensional applications and discrete-valued domains. While guiding the reader through the major achievements of this research area in a technically detailed yet accessible way, the book is concerned with the presentation and thorough (mathematical and experimental) investigation of a novel paradigm for probabilistic graphical modeling, the hybrid random field. This model subsumes and extends both Bayesian networks and Markov random fields. Moreover, it comes with well-defined learning algorithms, both for discrete and continuous-valued domains, which fit the needs of real-world applications involving large-scale, high-dimensional data.
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 Mo Probabilistic Graphical Models new edition
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Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning) - Daphne Koller, Nir Friedman
age level from 18 author daphne koller author nir friedman format hardback language english publication year 16 11 2009 series adaptive computation and machine learning series subject mathematics sciences subject 2 mathematics probabilistic graphical models principles and techniques adaptive computation author biography daphne koller is professor in the department of computer science at stanford university nir friedman is professor in the department of computer science and engineering at hebrew
TheNile.com.au About FAQ Payment Delivery Contact Us 1800-987-323 Probabilistic graphical models for pattern recognition and optical motion capture tracking. (English) by Stjepan Rajko Format Perfect Condition Brand New Language English This dissertation discusses several probabilistic graphical models which address problems in pattern recognition and optical motion capture tracking. It first introduces the augmented hidden Markov model with equivalence classes (AHMM+EC), which provides a unify
Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning), ISBN-13: 9780262013192, ISBN-10: 0262013193
Probabilistic Graphical Models Textbook
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
he 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 Mo
