# Posts tagged generalized linear model

## Out-Of-Sample Predictions

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

## GLM: Negative Binomial Regression

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

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

## Hierarchical Binomial Model: Rat Tumor Example

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 . Readers should already be familiar with the PyMC API.

## A Primer on Bayesian Methods for Multilevel Modeling

Hierarchical or multilevel modeling is a generalization of regression modeling.

## Bayesian regression with truncated or censored data

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

This tutorial shows an application of Bayesian Item Response Theory to NBA basketball foul calls data using PyMC. Based on Austin Rochford’s blogpost NBA Foul Calls and Bayesian Item Response Theory.

## Binomial regression

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

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