GPstuff - Gaussian process models for Bayesian analysis

Tree Approximate Message Passing

UAI 2015. Kernel-based just-in-time learning for expectation propagation

Expectation Particle Belief Propagation code

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.

Advanced Message Passing

A package to perform EP inference in a variety of settings

Probabilistic approach to neural nets - modern scalable approximate inference methods

Codes for 'Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models' (ICML 2023)

Maximum Likelihood for Gaussian Process Classifiers under Case-Control Sampling

[done] phd thesis @ oxford stats

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