FSharp Advent - 2020: Bayesian Inference in FSharp

This repository contains the code for my submission for the FSharp Advent calendar for 2020. My submission involves developing a framework for conducting Bayesian Inference in F#.

Prerequistes to Further Develop on the Notebook

• Jupyter Notebooks
• F# via .Net Core
• Dotnet Interactive

Bayesian Inference Resources

One of the best resources for learning Bayesian Statistical Methods is this playlist by Ben Lambert in addition to his book. A more mathematically involved book is this one by Peter Hoff.

Metropolis-Hastings

1. https://stephens999.github.io/fiveMinuteStats/MH_intro.html
2. https://bookdown.org/rdpeng/advstatcomp/metropolis-hastings.html
3. https://towardsdatascience.com/bayesian-statistics-metropolis-hastings-from-scratch-in-python-c3b10cc4382d
4. http://galton.uchicago.edu/~eichler/stat24600/Handouts/l12.pdf
5. https://www.jarad.me/courses/stat544/slides/Ch11/Ch11a.pdf
6. https://towardsdatascience.com/from-scratch-bayesian-inference-markov-chain-monte-carlo-and-metropolis-hastings-in-python-ef21a29e25a

Hamiltonian Monte Carlo

1. https://gregorygundersen.com/blog/2020/07/05/hmc/
2. https://arxiv.org/pdf/1701.02434.pdf
3. https://colcarroll.github.io/hamiltonian_monte_carlo_talk/bayes_talk.html#regression
4. http://diffsharp.github.io/DiffSharp/examples-hamiltonianmontecarlo.html

OLS Linear Regression

1. https://www.codesuji.com/2017/01/12/Linear-Regression-and-F/

FSharp Distributions

1. https://numerics.mathdotnet.com/Probability.html

Pymc Tutorials

1. https://docs.pymc.io/notebooks/stochastic_volatility.html

Bayesian Networks

1. http://dmg.org/pmml/v4-4/BayesianNetwork.html