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
repository
open issue
Index
