Variational Bayesian methods
Introduction
Bayesian inference
Probability Theory
Inference
Variational Bayesian approximation
Minimizing Kullback-Leibler divergence
Mean-field, fixed form
Variational Bayesian expectation maximization algorithm
Variational message passing
Stochastic variational inference
Non-conjugate methods
Lower bounding
Numerical integration
Conjugate exponential family interpretation
Tilted VB
Other methods
Riemannian conjugate gradient learning
Riemannian manifold
Algorithm
Improving optimization
Deterministic annealing
Parameter expansion
Pattern searches
Gaussian processes
Gaussian process regression
Variational sparse approximation
Uncertain inputs
Stochastic inference
Variational approximation of hyperparameters? Elsewhere?
GP-LVM?
Deep GPs?
Markov models
Smoothing in HMM
Smoothing in LSSM
Gaussian Markov random fields
General black-box framework
“Black box”
Variational approximation as linear regression
Gradient-based approximation
Inference model
Variational Bayesian methods
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Inference model
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Inference model
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inference or recognition model
deep learning / neural networks
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