A notebook exploring various Bayesian inference techniques and the algorithms behind them. Includes conjugate priors, Markov chain monte carlo (mcmc), and variational inference (vi)

Python scripts and Jupyter notebooks relating to the manuscript: Simon, H. and Huttley, G., 2021 Bayesian Inference of Joint Coalescence Times for Sampled Sequences. bioRxiv doi = 10.1101/2021.07.23.453461.

A Jupyter Notebook discussing Bayesian theory, Markov Chain Monte Carlo and the Affine Invariant Ensemble Sampler

Concepts of Bayesian Statistics, Bayesian inference, computational techniques and knowledge about the different types of models as well as model selection procedures.

Some notebooks for learning about bayesian models

A collection of notebooks that demonstrate Bayesian data analysis

Jupyter notebook for hands-on experience with PyMC3

Curated collection of notebooks and code files I have worked on while learning a wide range of data science subfields, such as Reinforcement Learning, Natural Language Processing, Deep Neural Networks, Genetic Algorithms, etc. Some of these are accompanied by a pdf and/or article.

Python notebooks for the paper "Testing Late-Time Cosmic Acceleration with uncorrelated Baryon Acoustic Oscillations dataset" by D. Benisty and D. Staicova. Please Cite as arXiv:2009.10701[astro-ph.CO]

Notebooks for Advanced Statistical Inference(ASI) course at EURECOM

StatististicalRethinking notebook project using Stan and Pluto notebooks.

StatisticalRethinking notebook project using Turing and Pluto notebooks (derived from Max Lapan's Jupyter project)