Stochastic processes and sampling using pyMC3 (python practicals)

Lecture notes for the stochastic processes lecture can be found here.

Instructions for setting up an environment that can be used for the practical sessions is available here.

Some additional lecture notes on advanced topics that will not be covered this year (Time Series, Nonparametric Regression and Gaussian/Dirichlet processes) can be downloaded here.

Lecture 1 (Mon, Nov. 22nd)
Stochastic processes and
sensitivity curves		NotesRecording
Lecture 2 (Wed, Nov. 24th)
Practical 1: frequentist
statistics in python

Note: Online only		Python notebook
Notebook with solutions		Recording
Lecture 3 (Th, Nov. 25th)
Practical 2: Bayesian sampling
in python using pyMC3

Note: Online only		Python notebook
Notebook with solutions		Recording
Lecture 4 (Fr, Nov. 26th)
Practical 3: GW population
inference

Note: Online only		Python notebook
Notebook with solutions		Recording