GPstuff - Gaussian process models for Bayesian analysis
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Updated
Dec 30, 2022 - MATLAB
GPstuff - Gaussian process models for Bayesian analysis
UAI 2015. Kernel-based just-in-time learning for expectation propagation
Tree Approximate Message Passing
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
Codes for 'Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models' (ICML 2023)
Advanced Message Passing
Maximum Likelihood for Gaussian Process Classifiers under Case-Control Sampling
Probabilistic approach to neural nets - modern scalable approximate inference methods
A package to perform EP inference in a variety of settings
[done] phd thesis @ oxford stats
Personal Website with Blogposts, Achievements and Ideas
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