Posts tagged generalized linear model
Bayesian regression with truncated or censored data
- 01 September 2022
- Category: beginner
The notebook provides an example of how to conduct linear regression when your outcome variable is either censored or truncated.
Rolling Regression
- 01 June 2022
- Category: intermediate
Pairs trading is a famous technique in algorithmic trading that plays two stocks against each other.
GLM: Negative Binomial Regression
- 01 June 2022
- Category: beginner
This notebook closely follows the GLM Poisson regression example by Jonathan Sedar (which is in turn inspired by a project by Ian Osvald) except the data here is negative binomially distributed instead of Poisson distributed.
NBA Foul Analysis with Item Response Theory
- 17 April 2022
- Category: intermediate, tutorial
This tutorial shows an application of Bayesian Item Response Theory [Fox, 2010] to NBA basketball foul calls data using PyMC. Based on Austin Rochford’s blogpost NBA Foul Calls and Bayesian Item Response Theory.
Binomial regression
- 01 February 2022
- Category: beginner
This notebook covers the logic behind Binomial regression, a specific instance of Generalized Linear Modelling. The example is kept very simple, with a single predictor variable.
GLM: Model Selection
- 08 January 2022
- Category: intermediate
A fairly minimal reproducible example of Model Selection using WAIC, and LOO as currently implemented in PyMC3.
Hierarchical Binomial Model: Rat Tumor Example
- 11 November 2021
- Category: intermediate
This short tutorial demonstrates how to use PyMC to do inference for the rat tumour example found in chapter 5 of Bayesian Data Analysis 3rd Edition [Gelman et al., 2013]. Readers should already be familiar with the PyMC API.
GLM: Mini-batch ADVI on hierarchical regression model
- 23 September 2021
- Category: intermediate
Unlike Gaussian mixture models, (hierarchical) regression models have independent variables. These variables affect the likelihood function, but are not random variables. When using mini-batch, we should take care of that.