Posts tagged generalized linear model
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.
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.
- 28 January 2023
Pairs trading is a famous technique in algorithmic trading that plays two stocks against each other.
- 10 January 2023
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.
- 08 December 2022
We want to fit a logistic regression model where there is a multiplicative interaction between two numerical features.
- 24 October 2022
Hierarchical or multilevel modeling is a generalization of regression modeling.
- 08 September 2022
The notebook provides an example of how to conduct linear regression when your outcome variable is either censored or truncated.
- 08 June 2022
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.
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.
- 08 February 2022
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.
- 08 January 2022
A fairly minimal reproducible example of Model Selection using WAIC, and LOO as currently implemented in PyMC3.
- 23 September 2021
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.