Notebooks and notes on statistics, computer science and philosophy, and some projects I've been working on (R/Python/C++).

  1. Mixed model reference implementations
    Concise reference implementations to fit (generalized) linear mixed models.
  2. Structure learning for Bayesian networks
    A notebook on structure MCMC to learn the structure of a Bayesian network.
  3. Truncated stick breaking in Greta
    If you want to see how to implement a truncated Dirichlet process for mixture modelling, check out this notebook.
  4. Simulation-based calibration
    SBC for validation of Bayesian inferences can be found here.
  5. Defining custom Greta distributions
    Greta comes with various distibutions for statistical modelling. In case you need one that Greta does not support, find a short tutorial how to do that here.
  6. Dirichlet process mixture models
    If you are interested in mixtures and nonparametric Bayes, check out my notebook on Dirichlet Process mixture models.
  7. Deep drama
    Deep drama implements a long short-term memory network for creating Greek drama. It uses drama from Euripides, Sophocles, Aristophanes and Aischylos from the Gutenberg project to train a recurrent neural network and then uses the trained model to write drama. In that sense it acts similar to other sequence models, just like HMMs.
  8. Philosophy of Science
    Some references on philosophy of science that are worth reading.
  9. Essential R
    I started compiling a small book about essential tools and libraries when writing R, especially for computational statistics and data science. You can find it here.
  10. Gaussian Processes
    Bayesian non-parametrics such as Gaussian Processes are a wonderful approach to machine learning. Check out my notebooks on regression and classification.