Durham University STFC CDT Data Science Workshop
Probabilistic Programming Lectures for the STFC CDT Workshop (September 13-17 2021) hosted by Durham University.
These lectures use the probabilistic programming language PyAutoFit (https://github.com/rhayes777/PyAutoFit).
Probabilistic Programming 1: Fitting a Model to Data
In this lecture, you will learn how to compose a probabilistic model and perform inference on data by defining a likelihood function, which is sampled using a non-linear search algorithm.
The slides for this section can be viewed at the following link (part 1 is covered between the first slide and the slide title "Cosmology: Strong Gravitational Lensing"):
https://github.com/Jammy2211/Probabilistic_Programming_CDT/blob/main/PyAutoFitCDT.pdf
Lecture materials can be found using the link below (which run in a web browser without any user setup required):
https://mybinder.org/v2/gh/Jammy2211/Probabilistic_Programming_CDT/main?filepath=lecture_1.ipynb
Probabilistic Programming 2: Application To Astronomy
This lecture uses strong gravitational lensing to demonstrate how adopting a probabilistic programming language can benefit scientific research and shows advanced model composition using multi-level models can create an extensible and easy to maintain code base.
The slides for this section can be viewed at the following link (part 2 is covered between the slide title "Cosmology: Strong Gravitational Lensing" and "PyAutoFit: Advanced Features"):
https://github.com/Jammy2211/Probabilistic_Programming_CDT/blob/main/PyAutoFitCDT.pdf
Lecture materials can be found using the links below (which run in a web browser without any user setup required):
(There is no part 2)