TutorialsΒΆ

Here are some simple tutorials, increasing in complexity, of how Bayesian inference may be used in the analysis of some data.

Some of these were heavily inspired by the tutorial paper of Hogg, Bovy, and Lang so for more information we recommend that. If you want to go even deeper, then check out Information Theory, Inference, and Learning Algorithms from David MacKay.

  1. Maximum likelihood
  2. Input Functions
  3. Markov chain Monte Carlo
  4. Using distributions
  5. Nested Sampling
  6. Custom priors