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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 Kullback-Leibler approximate inference for Bayesian generalized linear models. It is an implementation of the methods described in:
E. Challis and D. Barber. Gaussian Kullback-Leibler Approximate Inference. Journal of Machine Learning Research, vol. 14, 2013, pg. 2239-2286. http://jmlr.org/papers/v14/challis13a.html.
- Changes to previous version:
Code restructure and bug fix.
- BibTeX Entry: Download
- Corresponding Paper BibTeX Entry: Download
- Supported Operating Systems: Agnostic
- Data Formats: Matlab
- Tags: Classification, Approximate Inference, Sparse Learning, Variational Inference, Logistic Regression, Gaussian Processes, Generalized Linear Models, Bayesian Inference
- 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