# 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