Bayesian Learning and Neural Networks

Theoretical background

Probability Theory
Statistical Inference
Information Theoretic Quantities
Matching probabilistic models
NumPyro Basics

Conjugate bayesian models

Bayesian Linear Regression
Linear Latent Variable Models
An theoretical introduction to Gaussian processes

Approximate inference

Approximate Inference
Stochastic Variational Inference with NumPyro
Recent advances on VI
An introduction to Normalizing Flow models

Neural networks

My first Neural Network with flax
Non-linear LVMs: AutoEncoders
Variational Autoencoder
Bayesian Neural Networks with numpyro
More on Bayesian Neural Networks

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By Pablo Huijse Heise
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