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Variation Gaussian Approximate Inference for Bayesian Generalized Linear Models 1.0
by ed - April 9, 2011, 15:32:27 CET [
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- Description:
This package implements Variational Gaussian approximate inference for Bayesian Generalised Linear Models. It follows the methods as described in:
E. Challis and D. Barber. Concave Gaussian Variational Approximations for Inference in Large-Scale Bayesian Linear Models. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, Geoffrey Gordon, David Dunson, and Miroslav DudÃk, eds. JMLR W&CP, vol. 15 (draft), 2011.
which can be accessed on line at: http://web4.cs.ucl.ac.uk/staff/D.Barber/publications/challis_barber_aistats2011.pdf
- Changes to previous version:
Minor bug fix.
- BibTeX Entry: Download
- Corresponding Paper BibTeX Entry: Download
- Supported Operating Systems: Agnostic
- Data Formats: Matlab
- Tags: Classification, Sparse Learning, Logistic Regression, Generalized Linear Models
- Archive: download here
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Project details for Variation Gaussian Approximate Inference for Bayesian Generalized Linear Models
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Details
- Version:
- 1.0
- Submitter:
- ed
- Author(s):
- Edward Challis
- Posted:
- 9 April 2011
- Last Updated:
- 9 April 2011
- Open Source License:
- gnu gpl v3
- Programming Language(s):
- matlab