# Posts tagged generalized linear model

## Out-Of-Sample Predictions

- 20 December 2023
- beginner

We want to fit a logistic regression model where there is a multiplicative interaction between two numerical features.

## GLM: Negative Binomial Regression

- 20 September 2023
- beginner

This notebook uses libraries that are not PyMC dependencies and therefore need to be installed specifically to run this notebook. Open the dropdown below for extra guidance.

## Discrete Choice and Random Utility Models

This notebook uses libraries that are not PyMC dependencies and therefore need to be installed specifically to run this notebook. Open the dropdown below for extra guidance.

## Regression Models with Ordered Categorical Outcomes

Like many areas of statistics the language of survey data comes with an overloaded vocabulary. When discussing survey design you will often hear about the contrast between *design* based and *model* based approaches to (i) sampling strategies and (ii) statistical inference on the associated data. We won’t wade into the details about different sample strategies such as: simple random sampling, cluster random sampling or stratified random sampling using population weighting schemes. The literature on each of these is vast, but in this notebook we’ll talk about when any why it’s useful to apply model driven statistical inference to Likert scaled survey response data and other kinds of ordered categorical data.

## Rolling Regression

- 28 January 2023
- intermediate

Pairs trading is a famous technique in algorithmic trading that plays two stocks against each other.

## Hierarchical Binomial Model: Rat Tumor Example

- 10 January 2023
- 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.

## A Primer on Bayesian Methods for Multilevel Modeling

- 24 October 2022
- intermediate

Hierarchical or multilevel modeling is a generalization of regression modeling.

## Bayesian regression with truncated or censored data

- 20 September 2022
- beginner

The notebook provides an example of how to conduct linear regression when your outcome variable is either censored or truncated.

## NBA Foul Analysis with Item Response Theory

- 17 April 2022
- tutorial, intermediate

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

- 20 February 2022
- 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
- intermediate

A fairly minimal reproducible example of Model Selection using WAIC, and LOO as currently implemented in PyMC3.

## GLM: Mini-batch ADVI on hierarchical regression model

- 23 September 2021
- 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.