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ORCIDLearning in Graphical Models, 1998
- Michael I. Jordan:
Learning in Graphical Models. NATO ASI Series 89, Springer Netherlands 1998, ISBN 978-94-010-6104-9 - Robert Cowell:
Introduction to Inference for Bayesian Networks. 9-26 - Robert Cowell:
Advanced Inference in Bayesian Networks. 27-49 - Uffe Kjærulff:
Inference in Bayesian Networks Using Nested Junction Trees. 51-74 - Rina Dechter:
Bucket Elimination: A Unifying Framework for Probabilistic Inference. 75-104 - Michael I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola, Lawrence K. Saul:
An Introduction to Variational Methods for Graphical Models. 105-161 - Tommi S. Jaakkola, Michael I. Jordan:
Improving the Mean Field Approximation Via the Use of Mixture Distributions. 163-173 - David John Cameron MacKay:
Introduction to Monte Carlo Methods. 175-204 - Radford M. Neal:
Suppressing Random Walks in Markov Chain Monte Carlo Using Ordered Overrelaxation. 205-228 - Thomas S. Richardson:
Chain Graphs and Symmetric Associations. 231-259 - Milan Studený
, Jirina Vejnarová:
The Multiinformation Function as a Tool for Measuring Stochastic Dependence. 261-297 - David Heckerman:
A Tutorial on Learning with Bayesian Networks. 301-354 - Radford M. Neal, Geoffrey E. Hinton:
A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants. 355-368 - Christopher M. Bishop:
Latent Variable Models. 371-403 - Joachim M. Buhmann:
Stochastic Algorithms for Exploratory Data Analysis: Data Clustering and Data Visualization. 405-419 - Nir Friedman, Moisés Goldszmidt:
Learning Bayesian Networks with Local Structure. 421-459 - Dan Geiger, David Heckerman, Christopher Meek:
Asymptotic Model Selection for Directed Networks with Hidden Variables. 461-477 - Geoffrey E. Hinton, Brian Sallans, Zoubin Ghahramani:
A Hierarchical Community of Experts. 479-494 - Michael J. Kearns, Yishay Mansour, Andrew Y. Ng:
An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering. 495-520 - Stefano Monti, Gregory F. Cooper:
Learning Hybrid Bayesian Networks from Data. 521-540 - Lawrence K. Saul, Michael I. Jordan:
A Mean Field Learning Algorithm for Unsupervised Neural Networks. 541-554 - Peter W. F. Smith, Joe Whittaker:
Edge Exclusion Tests for Graphical Gaussian Models. 555-574 - David J. Spiegelhalter, Nicky Best, W. R. Gilks, H. Inskip:
Hepatitis B: A Case Study in MCMC. 575-598 - Christopher K. I. Williams:
Prediction with Gaussian Processes: From Linear Regression to Linear Prediction and Beyond. 599-621
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