Graphical models, a marriage between probability theory and graph theory, provide a natural tool for dealing with two problems that occur throughout applied mathematics and engineering--uncertainty and complexity. In particular, they play an increasingly important role in the design and analysis of machine learning algorithms. Fundamental to the idea of a graphical model is the notion of modularity: a complex system is built by combining simpler parts. Probability theory serves as the glue whereby the parts are combined, ensuring that the system as a whole is consistent and providing ways to interface models to data. Graph theory provides both an intuitively appealing interface by which humans can model highly interacting sets of variables and a data structure that lends itself naturally to the design of efficient general-purpose algorithms.This book presents an in-depth exploration of issues related to learning within the graphical model formalism. Four chapters are tutorial chapters--Robert Cowell on Inference for Bayesian Networks, David MacKay on Monte Carlo Methods, Michael I. Jordan et al. on Variational Methods, and David Heckerman on Learning with Bayesian Networks. The remaining chapters cover a wide range of topics of current research interest.
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"The state of the art presented by the experts in the field." Ross D. Shachter , Department of Engineering-Economic Systemsand Operations Research, Stanford University
About the Author
Michael I. Jordan is Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley.
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24 of 28 people found the following review helpful:
4.0 out of 5 stars
Recommended, but not the place to begin,
By Todd Ebert (Long Beach California) - See all my reviews
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This review is from: Learning in Graphical Models (Adaptive Computation and Machine Learning) (Paperback)
The title of the book is somewhat misleading, in that most of the research papers involve advanced issues concerning one particular graphical model, namely the Bayesian network. For this reason I highly recommend, as a prerequisite to this book, Finn Jensen's "Bayesian Networks and Decision Graphs". Jensen's book is adequate in giving a good introduction and overview of the subject, but not sufficient for calling oneself an "expert" upon successfully digesting it. To its credit, "Learning in Graphical Models" has several well-written and interesting papers, but the tutorial papers just did not seem enough of an introduction for me to feel comfortable using it as a first source of introduction. What I find most compelling about Bayesian networks is the fact that they seem both highly modular (which facilitates reusability and network interconnectivity) and can be designed in a semi-rational manner (contrast this with neural-network architectures for which few good algorithms exist for determining size and number of layers). For this reason I imagine they will be important players in future engineering projects that require learning and adaptation.
0 of 1 people found the following review helpful:
5.0 out of 5 stars
Good book for machine learning and graphical models,
By Iftekhar Naim (Rochester, USA) - See all my reviews
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This review is from: Learning in Graphical Models (Adaptive Computation and Machine Learning) (Paperback)
The book contains a nice collection of papers relevant to Graphical models and machine learning. The book came in good condition and organization of topics is great!!! Some of the compiled papers are available online for free. However, I found the book to be useful because of its organization, logical flow and compilation of very relevant and useful papers related to the field.
11 of 28 people found the following review helpful:
5.0 out of 5 stars
Simply Superb...,
By A Customer
This review is from: Learning in Graphical Models (Adaptive Computation and Machine Learning) (Paperback)
My area of research revolves around graphical models... Best Book... The book that introduced me as to how effective graphical models are... As stated in the editorial review, graphical model is the marriage between graph theory and probability and this book justifies the sacredness of this marriage!
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The field of Bayesian networks, and graphical models in general, has grown enormously over the last few years, with theoretical and computational developments in many areas. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
multiinformation function, ordered overrelaxation, regular inference rule, neighbouring cliques, top binary unit, outward neighbours, root clique, independency models, top level binary unit, variational transformation, graphical model formalism, junction trees approach, minimum space cost, thumbtack problem, inward neighbour, partitioned density, sigmoid belief networks, random subvector, posterior partition, moral graph, probabilistic soundness, modified profile likelihood, partition loss, induced width, junction tree algorithm Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo, Morgan Kaufmann, New York, San Francisco, San Mateo, Journal of the Royal Statistical Society, Neural Information Processing Systems, Space Time, University of Toronto, John Wiley, Department of Computer Science, Journal of the American Statistical Association, Microsoft Research, Oxford University Press, Kluwer Academic Publishers, Stanford University, Tenth Conference, Annals of Statistics, Lecture Notes, University of California, Aalborg University, Aston University, Menlo Park, Operations Research, Redwood City New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
The field of Bayesian networks, and graphical models in general, has grown enormously over the last few years, with theoretical and computational developments in many areas. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
multiinformation function, ordered overrelaxation, regular inference rule, neighbouring cliques, top binary unit, outward neighbours, root clique, independency models, top level binary unit, variational transformation, graphical model formalism, junction trees approach, minimum space cost, thumbtack problem, inward neighbour, partitioned density, sigmoid belief networks, random subvector, posterior partition, moral graph, probabilistic soundness, modified profile likelihood, partition loss, induced width, junction tree algorithm Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo, Morgan Kaufmann, New York, San Francisco, San Mateo, Journal of the Royal Statistical Society, Neural Information Processing Systems, Space Time, University of Toronto, John Wiley, Department of Computer Science, Journal of the American Statistical Association, Microsoft Research, Oxford University Press, Kluwer Academic Publishers, Stanford University, Tenth Conference, Annals of Statistics, Lecture Notes, University of California, Aalborg University, Aston University, Menlo Park, Operations Research, Redwood City New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 83
books:
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100
books
cite this book:
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- Markov Chain Monte Carlo in Practice (Chapman & Hall/CRC Interdisciplinary Statistics) by W.R. Gilks on 6 pages
- Advances in Neural Information Processing Systems 12 by Sara A. Solla on 5 pages
- Uncertainty in Artificial Intelligence (Volume 4 of Machine Intelligence and Pattern Recognition) (Vol 1) by R. K. Ghatnagar on 5 pages
- Elements of Information Theory 2nd Edition (Wiley Series in Telecommunications and Signal Processing) by Thomas M. Cover on 5 pages
- Graphical Models (Oxford Statistical Science Series) by Steffen L. Lauritzen on page 258, page 554, and page 568
See all 83 books this book cites
- Advanced Mean Field Methods: Theory and Practice (Neural Information Processing) by Manfred Opper on 9 pages
- Probabilistic Modelling in Bioinformatics and Medical Informatics by Dirk Husmeier on 7 pages
- Journal of Artificial Intelligence Research, Volume 10 (JAIR) by Author Unknown on 4 pages
- Bayesian Artificial Intelligence (Chapman & Hall/CRC Computer Science & Data Analysis) by Kevin B. Korb on 4 pages
- Research in Computational Molecular Biology: 9th Annual International Conference, RECOMB 2005, Cambridge, MA, USA, May 14-18, 2005, Proceedings ... Science / Lecture Notes in Bioinformatics) by Satoru Miyano on page 30, page 46, and page 422
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