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FSharp Advent - 2020: Bayesian Inference in FSharp

Bayes

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

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