A package to perform EP inference in a variety of settings
-
Updated
Aug 20, 2017 - Julia
A package to perform EP inference in a variety of settings
Probabilistic approach to neural nets - modern scalable approximate inference methods
[done] phd thesis @ oxford stats
Personal Website with Blogposts, Achievements and Ideas
Advanced Message Passing
Codes for 'Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models' (ICML 2023)
Maximum Likelihood for Gaussian Process Classifiers under Case-Control Sampling
Knowledge elicitation when the user can give feedback to different features of the model with the goal to improve the prediction on the test data in a "smal n, large p" setting.
Expectation Particle Belief Propagation code
Tree Approximate Message Passing
UAI 2015. Kernel-based just-in-time learning for expectation propagation
GPstuff - Gaussian process models for Bayesian analysis
Add a description, image, and links to the expectation-propagation topic page so that developers can more easily learn about it.
To associate your repository with the expectation-propagation topic, visit your repo's landing page and select "manage topics."