Chris Fonnesbeck: An introduction to Markov Chain Monte Carlo using PyMC3 | PyData London 2019

Repo - github.com/fonnesbeck/mcmc_py...
Markov chain Monte Carlo (MCMC) is the most common approach for performing Bayesian data analysis. MCMC is a general class of algorithms that uses simulation to estimate a variety of statistical models. This tutorial will introduce users how to use MCMC for fitting statistical models using PyMC3, a Python package for probabilistic programming.
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[@stepkurniawan]

1:47 I. Introduction to PYMC 11:13 Case Study: Radon contamination 21:53 Prediction 22:48 Model Checking 24:10 Prior Sensitivity 25:29 II. Markov Chain Monte Carlo 31:19 Metropolis-Hastings 33:38 Random-walk Metropolis Hastings 34:36 Example: Linear Regression 39:37 Auto-tuning Metropolis-Hastings 41:31 Hamiltonian Monte Carlo 44:35 Simulting Hamiltonian Dynamics 51:28 III. Building Models in PyMC 52:46 Example: Coal Mining Disaster 56:21 The FreeRV class 59:06 Manual probability distribution 1:00:51 The Observed RV Appreciate your video! Thank you!

[@vinayramasahayam2410]

00:00 Introduction to PYMC 26:00 MCMC