Hybrid Random Fields

1. Front Matter

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2. Introduction

   • Antonino Freno, Edmondo Trentin

   Pages 1-14

3. Bayesian Networks

   • Antonino Freno, Edmondo Trentin

   Pages 15-41

4. Markov Random Fields

   • Antonino Freno, Edmondo Trentin

   Pages 43-68

5. Introducing Hybrid Random Fields: Discrete-Valued Variables

   • Antonino Freno, Edmondo Trentin

   Pages 69-86

6. Extending Hybrid Random Fields: Continuous-Valued Variables

   • Antonino Freno, Edmondo Trentin

   Pages 87-119

7. Applications

   • Antonino Freno, Edmondo Trentin

   Pages 121-150

8. Probabilistic Graphical Models: Cognitive Science or Cognitive Technology?

   • Antonino Freno, Edmondo Trentin

   Pages 151-162

9. Conclusions

   • Antonino Freno, Edmondo Trentin

   Pages 163-167

10. Back Matter

    PDF

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.

• Bayesian Networks
• Data Mining
• Density Estimation
• Hybrid Random Fields
• Intelligent Systems
• Kernel Methods
• Machine Learning
• Markov Random Fields
• Probabilistic Graphical Models

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)

• Dipartimento di Ingegneria dell’Informazione, Università degli Studi di Siena , Siena, Italy

  Antonino Freno, Edmondo Trentin

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