Overview
- Covers the concepts and techniques related to the hybrid random field model for the first time
- Offers a self-contained introduction to semiparametric and nonparametric density estimation
- Written by leading experts in the field
Part of the book series: Intelligent Systems Reference Library (ISRL, volume 15)
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Table of contents (8 chapters)
Keywords
About this book
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 Universitet
The 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, Università degli Studi di Siena
Graphical 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.
Reviews
From the reviews:
“This book presents novel probabilistic graphical models, i.e., hybrid random fields. … the authors have written a very valuable book – rigorous in the treatment on the mathematical background, but also enriched with a very open view of the field, full of stimulating connections.” (Jerzy Martyna, zbMATH, Vol. 1278, 2014)Authors and Affiliations
Bibliographic Information
Book Title: Hybrid Random Fields
Book Subtitle: A Scalable Approach to Structure and Parameter Learning in Probabilistic Graphical Models
Authors: Antonino Freno, Edmondo Trentin
Series Title: Intelligent Systems Reference Library
DOI: https://doi.org/10.1007/978-3-642-20308-4
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer Berlin Heidelberg 2011
Hardcover ISBN: 978-3-642-20307-7Published: 26 May 2011
Softcover ISBN: 978-3-642-26818-2Published: 15 July 2013
eBook ISBN: 978-3-642-20308-4Published: 11 April 2011
Series ISSN: 1868-4394
Series E-ISSN: 1868-4408
Edition Number: 1
Number of Pages: XVIII, 210