(1/6) Bayesian inference workflow in astronomy and physics
Channel: JOHANNES BUCHNER
Date: 2021-12-02

* modelling astrophysical observations based on cepheids
* probabilistic generative models
* systematic and statistical uncertainty
* priors, likelihoods, posteriors, evidence
* prior predictive checks
* posterior predictive checks
* marginal posteriors
* Bayesian model comparison with posterior odds and Bayes factors
* Numerical computation of all the above

The second video continues this material. 

Intentionally, this tutorial uses grids, to avoid the complexity of Monte Carlo integration algorithms. They are introduced in the subsequent lectures.

from Block I: Monte Carlo inference methods
of the course "Practical Inference for Researchers in the Physical Sciences"
https://johannesbuchner.github.io/PracticalInferenceForResearchersInThePhysicalSciencesCourse/
aimed at the graduate and post-graduate level (master, PhD, postdoc)
for physicists, cosmologists, astronomers, etc.
at TU Munich, 2021

Lecturer: Johannes Buchner