Status: Draft
Bayesian Learning
For the course content, see Bayesian Learning.
Prerequisite
- exercise-expected-value
- exercise-variance-sample-size-dependence
- exercise-biased-monte-carlo-estimator
- exercise-entropy
- exercise-kullback-leibler-divergence
- exercise-multivariate-gaussian
- exercise-bayes-rule
- exercise-jensen-inequality
(Maximum) likelihood and Maximum a posterior
Bayesian Networks
- exercise-bayesian-networks-by-example
- exercise-d-separation
- exercise-forward-reasoning-probability-tables
- exercise-sensorfusion-and-kalman-filter-1d
EM-Algorithm
Monte-Carlo / MCMC / Sampling
- exercise-inverse-transform-sampling
- exercise-importance-sampling
- exercise-rejection-sampling
- exercise-MCMC-Metropolis-sampling
Variance Reduction Techniques
- exercise-variance-reduction-by-control-variates
- exercise-variance-reduction-by-reparametrization
- exercise-variance-reduction-via-rao-blackwellization
- exercise-variance-reduction-by-importance-sampling
Variational Methods
- exercise-variational-mean-field-approximation-for-a-simple-gaussian
- exercise-variational-EM-bayesian-linear-regression
Probabilistic Programming
- exercise-pyro-simple-gaussian
- exercise-pymc3-examples
- exercise-pymc3-bundesliga-predictor
- exercise-pymc3-ranking
Bayesian Deep Learning Examples
- For the exercises you need dp.py.
- exercise-variational-autoencoder
- exercise-bayesian-by-backprop