1998 LearningInGraphicalModels

• (Jordan, 1998) ⇒ Michael I. Jordan (editor). (1998). “Learning in Graphical Models.” ISBN 978-94-010-6104-9,978-94-011-5014-9. DOI: 10.1007/978-94-011-5014-9. Springer. In: Part of NATO ASI Series book series (ASID, volume 89).

Subject Headings: Graphical Model Training.

Notes

• Collection of papers. Discusses approximate inference.

Quotes

Publisher's Synopsis

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.

PART I: INFERENCE

• (Cowell, 1998) ⇒ Robert Cowell (1998). “Introduction to Inference for Bayesian Networks". In: Michael I. Jordan (eds) “Learning in Graphical Models”. Springer. DOI: 10.1007/978-94-011-5014-9_1
• (Cowell, 1998) ⇒ Robert Cowell (1998). “Advanced Inference in Bayesian Networks". In: Michael I. Jordan (eds) “Learning in Graphical Models”. Springer. DOI: 10.1007/978-94-011-5014-9_2
• (Kjaerulff, 1998) ⇒ Uffe Kjaerulff (1998). “Inference in Bayesian Networks using Nested Junction Trees". In: Michael I. Jordan (eds) “Learning in Graphical Models”. Springer. DOI: 10.1007/978-94-011-5014-9_3.
• (Dechter, 1998) ⇒ R. Dechter (1998). “Bucket Elimination: A Unifying Framework for Probabilistic Inference". In: Michael I. Jordan (eds) “Learning in Graphical Models”. Springer. DOI: 10.1007/978-94-011-5014-9_4.
• (Jordan et al., 1998) ⇒ Michael I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola, and Lawrence K. Saul (1998). “An Introduction to Variational Methods for Graphical Models". In: Michael I. Jordan (eds) “Learning in Graphical Models”. Springer. DOI: 10.1007/978-94-011-5014-9_5.
• (Jaakkola & Jordan, 1998) ⇒ Tommi S. Jaakkola, and Michael I. Jordan. (1998). “Improving the Mean Field Approximation Via the Use of Mixture Distributions". In: Michael I. Jordan (eds) “Learning in Graphical Models”. Springer. DOI: 10.1007/978-94-011-5014-9_6.
• (Mackay, 1998) ⇒ D. J. C. Mackay. (1998). “Introduction to Monte Carlo Methods". In: Michael I. Jordan (eds) “Learning in Graphical Models”. Springer. DOI: 10.1007/978-94-011-5014-9_7.
• (Neal, 1998) ⇒ Radford M. Neal. (1998). “Suppressing Random Walks in Markov Chain Monte Carlo Using Ordered Overrelaxation". In: Michael I. Jordan (eds) “Learning in Graphical Models”. Springer. DOI: 10.1007/978-94-011-5014-9_8.

PART II: INDEPENDENCE

• (Richardson, 1998) ⇒ Thomas S. Richardson (1998). “Chain Graphs and Symmetric Associations". In: Michael I. Jordan (eds) “Learning in Graphical Models”. Springer. DOI: 10.1007/978-94-011-5014-9_9
• (Studeny & Vejnarova, 1998) ⇒ M. Studeny, and J. Vejnarova(1998). “The Multi-information Function as a Tool for Measuring Stochastic Dependence". In: Michael I. Jordan (eds) “Learning in Graphical Models”. Springer. DOI: 10.1007/978-94-011-5014-9_10

PART III: FOUNDATIONS FOR LEARNING

• (Heckerman, 1998) ⇒ David Heckerman (1998). “A Tutorial on Learning with Bayesian Networks". In: Michael I. Jordan (eds) “Learning in Graphical Models”. Springer. DOI: 10.1007/978-94-011-5014-9_11.
• (Neal & Hinton, 1998) ⇒ Radford M. Neal, and Geoffrey E. Hinton (1998). “A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants". In: Michael I. Jordan (eds) “Learning in Graphical Models”. Springer. DOI: 10.1007/978-94-011-5014-9_12.

PART IV: LEARNING FROM DATA

• (Bishop, 1998) ⇒ Christopher M. Bishop (1998). “Latent Variable Models". In: Michael I. Jordan (eds) “Learning in Graphical Models”. Springer. DOI: 10.1007/978-94-011-5014-9_13.
• (Buhmann, 1998) ⇒ Joachim M. Buhmann (1998). “Stochastic Algorithms for Exploratory Data Analysis: Data Clustering and Data Visualization". In: Michael I. Jordan (eds) “Learning in Graphical Models”. Springer. DOI: 10.1007/978-94-011-5014-9_14.
• (Friedman & Goldszmidt, 1998) ⇒ Nir Friedman, and Moises Goldszmidt (1998). “Learning Bayesian Networks with Local Structure". In: Michael I. Jordan (eds) “Learning in Graphical Models”. Springer. DOI: 10.1007/978-94-011-5014-9_15.
• (Geiger et al., 1998) ⇒ Dan Geiger, David Heckerman, and Christopher Meek (1998). “Asymptotic Model Selection for Directed Networks with Hidden Variables". In: Michael I. Jordan (eds) “Learning in Graphical Models”. Springer. DOI: 10.1007/978-94-011-5014-9_16.
• (Hinton et al., 1998) ⇒ Geoffrey E. Hinton, Brian Sallans, and Zoubin Ghahramani (1998). “A Hierarchical Community of Experts". In: Michael I. Jordan (eds) “Learning in Graphical Models”. Springer. DOI: 10.1007/978-94-011-5014-9_17.
• (Kearns et al., 1998) ⇒ Michael Kearns, Yishay Mansour, and Andrew Y. Ng (1998). “An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering". In: Michael I. Jordan (eds) “Learning in Graphical Models”. Springer. DOI: 10.1007/978-94-011-5014-9_18.
• (Monti & Cooper, 1998) ⇒ Stefano Monti, and Gregory F. Cooper (1998). “Learning Hybrid Bayesian Networks from Data". In: Michael I. Jordan (eds) “Learning in Graphical Models”. Springer. DOI: 10.1007/978-94-011-5014-9_19.
• (Saul & Jordan, 1998) ⇒ Lawrence Saul, and Michael Jordan (1998). “A Mean Field Learning Algorithm for Unsupervised Neural Networks". In: Michael I. Jordan (eds) “Learning in Graphical Models”. Springer. DOI: 10.1007/978-94-011-5014-9_20.
• (Smith & Whittaker, 1998) ⇒ Peter W. F. Smith, and Joe Whittaker (1998). “Edge Exclusion Tests for Graphical Gaussian Models". In: Michael I. Jordan (eds) “Learning in Graphical Models”. Springer. DOI: 10.1007/978-94-011-5014-9_21.
• (Spiegelhalter et al., 1998) ⇒ D. J. Spiegelhalter, N. G. Best, W. R. Gilks, and H. Inskip (1998). “Hepatitis B: A Case Study in MCMC". In: Michael I. Jordan (eds) “Learning in Graphical Models”. Springer. DOI: 10.1007/978-94-011-5014-9_22.
• (Williams, 1998) ⇒ C. K. I. Williams, and H. Inskip (1998). “Prediction with Gaussian Processes: From Linear Regression to Linear Prediction and Beyond". In: Michael I. Jordan (eds) “Learning in Graphical Models”. Springer. DOI: 10.1007/978-94-011-5014-9_23.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
1998 LearningInGraphicalModels				Learning in Graphical Models			http://books.google.com/books?id=7f61BBKdJ4EC			1998