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- Dmitry M. Malioutov, Jason K. Johnson, Alan S. Willsky
- Journal of Machine Learning Research
- 2006

We present a new framework based on walks in a graph for analysis and inference in Gaussian graphical models. The key idea is to decompose the correlation between each pair of variables as a sum overâ€¦ (More)

- Jason K. Johnson, Dmitry M. Malioutov, Alan S. Willsky
- ArXiv
- 2007

We develop a general framework for MAP estimation in discrete and Gaussian graphical models using Lagrangian relaxation techniques. The key idea is to reformulate an intractable estimation problem asâ€¦ (More)

- Venkat Chandrasekaran, Jason K. Johnson, Alan S. Willsky
- IEEE Transactions on Signal Processing
- 2008

Graphical models provide a powerful formalism for statistical signal processing. Due to their sophisticated modeling capabilities, they have found applications in a variety of fields such as computerâ€¦ (More)

This paper presents a new framework based on walks in a graph f or analysis and inference in Gaussian graphical models. The key ide a is to decompose correlations between variables as a sum over allâ€¦ (More)

- Jason K. Johnson
- 2008

Graphical models provide compact representations of complex probability distributions of many random variables through a collection of potential functions defined on small subsets of these variables.â€¦ (More)

- Jason K. Johnson, Alan S. Willsky
- IEEE Transactions on Image Processing
- 2008

This paper presents recursive cavity modeling - a principled, tractable approach to approximate, near-optimal inference for large Gauss-Markov random fields. The main idea is to subdivide the randomâ€¦ (More)

- Dmitry M. Malioutov, Jason K. Johnson, Myung Jin Choi, Alan S. Willsky
- IEEE Transactions on Signal Processing
- 2008

We present a versatile framework for tractable computation of approximate variances in large-scale Gaussian Markov random field estimation problems. In addition to its efficiency and simplicity, itâ€¦ (More)

- Jason K. Johnson, Danny Bickson, Danny Dolev
- IEEE International Symposium on Informationâ€¦
- 2009

Gaussian belief propagation (GaBP) is an iterative message-passing algorithm for inference in Gaussian graphical models. It is known that when GaBP converges it converges to the correct MAP estimateâ€¦ (More)

- Dmitry M. Malioutov, Jason K. Johnson, Alan S. Willsky
- IEEE International Conference on Acoustics Speechâ€¦
- 2006

We consider the problem of variance estimation in large-scale Gauss-Markov random field (GMRF) models. While approximate mean estimates can be obtained efficiently for sparse GMRFs of very largeâ€¦ (More)

- Jason K. Johnson, Venkat Chandrasekaran, Alan S. Willsky
- AISTATS
- 2007

We propose a new approach for learning a sparse graphical model approximation to a specified multivariate probability distribution (such as the empirical distribution of sample data). The selectionâ€¦ (More)