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Bayesian Modelling in Python

Created/Published/Taught by:

Mark Regan

Content Found Via:

Mark Regan

Free? Yes

Tags: A/B testing / bayesian models / Bayesian statistics / Markov Chain Monte Carlo (MCMC) / mixed effects / optimization / python / t-test

Content Type: References, Learning Guides, Etc. / Tutorial

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“Welcome to ‘Bayesian Modelling in Python’ – a tutorial for those interested in learning how to apply bayesian modelling techniques in python (PYMC3). This tutorial doesn’t aim to be a bayesian statistics tutorial – but rather a programming cookbook for those who understand the fundamental of bayesian statistics and want to learn how to build bayesian models using python. The tutorial sections and topics can be seen below.

Contents

- Introduction
- Motivation for learning bayesian statistics
- Loading and parsing Hangout chat data

- Section 1: Estimating model parameters
- Frequentist technique for estimating parameters of a poisson model (Optimization routine)
- Bayesian technique for estimating parameters of a poisson model (MCMC)

- Section 2: Model checking & comparison
- Posterior predictive check
- Bayes factor

- Section 3: Hierarchal modelling
- Model pooling (separate models)
- Partial pooling (hierarchal models)
- Shrinkage effect of partial pooling

- Section 4: Bayesian regression
- Bayesian fixed effects poisson regression
- Bayesian mixed effects poisson regression

- Section 5: Bayesian survival analysis
- Survival model theory
- Cox proportional hazard model
- Aalen’s additive hazard model

- Section 6: Bayesian A/B tests
- Bavesian test of proportions
- Bayesian t-test (BEST)”

Recommended Prerequisites: none specified

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