Notebooks and notes on statistics, computer science and philosophy, and some projects I've been working on (R/Python/C++).
Structure learning for Bayesian networks
A notebook on structure MCMC
to learn the structure of a Bayesian network.
Truncated stick breaking in Greta
If you want to see how to implement a truncated Dirichlet process for mixture modelling, check out
SBC for validation of Bayesian inferences can be found
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
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.
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
Bayesian non-parametrics such as Gaussian Processes are a wonderful approach to machine
learning. Check out my notebooks on