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