This is a repository of Jupyter notebooks used by Florent Leclercq during lectures on Bayesian statistics and Information Theory. The homepage of the lectures is accessible here.
- Ignorance priors (exemplified with the lighthouse problem) and the maximum entropy principle
- Gaussian random fields:
- Examples and a digression on non-Gaussianity
- Bayesian signal processing and reconstruction: de-noising (example, example with the CMB)
- Bayesian signal processing and reconstruction: de-blending
- Bayesian decision theory
- Markov Chain Monte Carlo:
- Approximate Bayesian Computation:
- Information theory:
FL thanks Benjamin Wandelt for his own lectures, which have inspired a fraction of this material.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.