# Monte Carlo Methods

* Likelihood, Prior, Posterior
* Propagation of Uncertainties
* Metropolis-Hastings MCMC [from scratch](https://github.com/fbartolic/fitting_model_to_data/blob/master/fitting_model_to_data.ipynb)
* Affine-invariant MCMC using [emcee](https://emcee.readthedocs.io/en/stable/)
* Nested Sampling
  * [Code: dynesty](https://dynesty.readthedocs.io/en/stable/)
  * [Code: ultranest](https://johannesbuchner.github.io/UltraNest/index.html)
* Hamiltonian Monte Carlo (HMC)
  * [Article: A Conceptual Introduction to HMC](https://arxiv.org/abs/1701.02434)
  * No U-Turn Sampler using [pymc](https://www.pymc.io/projects/examples/en/latest/gallery.html)
* Sampling methods [demo](https://chi-feng.github.io/mcmc-demo/app.html)
  * [Samplers from Scratch](https://nbviewer.org/github/JohannesBuchner/PracticalInferenceForResearchersInThePhysicalSciencesCourse/tree/main/)
  * Implementation using [bilby](https://bilby-dev.github.io/bilby/compare_samplers.html)
* Convergence Tests
* Autocorrelation
* Evidence
* Model Comparison

## Reporting MCMC Results

* [Article](https://pkgw.github.io/mcmc-reporting/)
* Posteriors vs Point Estimates
* Percentiles