- Choosing priors, Maximum Entropy
- Model evidence
- Bayes factor
- Bayesian hypothesis testing
- Lecture notes in pdf or html
- Bayesian model selection [ipynb]
- Bayes in the sky: Bayesian inference and model selection in cosmology by Robert Trotta.
- Why every statistician should know about cross-validation blog post by Rob J Hyndman.
- Summary of prior choice discussion, Maximum Entropy for reconstructing functions
- Recap of 2019-06-17 exercises: Prior sensitivity of model evidence
- Evidence when there is a naturalness prior
- Computational issues for evidence and alternatives
- Scanned lecture notes [pdf]
- Bayes in the sky: Bayesian inference and model selection in cosmology by Robert Trotta.
- Bayesian model selection revisited [ipynb]
- Evidence for EFT coefficients [ipynb]
- EFT slides II [pdf]
- Gaussian processes as infinite-dimensional Gaussian distributions
- From parametric models to Gaussian processes
- Covariance functions
- Scanned lecture notes [pdf]
- Gaussian processes - Part I ipynb
- Gaussian process emulators
- Scanned lecture notes [pdf]
- A Bayesian Approach for Parameter Estimation and Prediction using a Computationally Intensive Model by D. Higdon et al.
- Gaussian process models for regression
- Gaussian process emulators
- Scanned lecture notes [pdf]
- A Bayesian Approach for Parameter Estimation and Prediction using a Computationally Intensive Model by D. Higdon et al.
- Visualization of Hamiltonian Monte Carlo (HMC) and the No-U-Turn Sampler (NUTS)
- Physics of HMC
- PyMC3 overview
- Scanned lecture notes
- HMC visualization I (Richard McElreath)
- HMC visualization II (Alex Rogozhnikov)
- Bettencourt, A conceptual introduction to Hamiltonian Monte Carlo
- Neal, MCMC using Hamiltonian dynamics
- PyMC3: Introduction [ipynb]
- PyMC3 Docs: Getting started [ipynb]
- PyMC3 Docs: Quick start [ipynb]
- PyMC3 linear regression example (from Duke course)[ipynb]
- PyMC3: Rob Hicks Bayesian 8 [ipynb] Shows a comparison between Gibbs sampling, PyMC3, and emcee plus an example of using corner with PyMC3 output.
- Liouville theorem visualization [ipynb]
- Orbital equations solved with different algorithms, including 2nd-order leapfrog [ipynb]
- Systematic errors: offset, normalization uncertainty, other experimental systematics, theory systematic from an EFT