# Posts tagged

## 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.

## 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.

## Gaussian Processes using numpy kernel

- 31 July 2022
- advanced

Example of simple Gaussian Process fit, adapted from Stan’s example-models repository.

## General API quickstart

- 31 May 2022
- beginner

Models in PyMC are centered around the `Model`

class. It has references to all random variables (RVs) and computes the model logp and its gradients. Usually, you would instantiate it as part of a `with`

context:

## Bayesian moderation analysis

- 20 March 2022
- beginner

This notebook covers Bayesian moderation analysis. This is appropriate when we believe that one predictor variable (the moderator) may influence the linear relationship between another predictor variable and an outcome. Here we look at an example where we look at the relationship between hours of training and muscle mass, where it may be that age (the moderating variable) affects this relationship.

## 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.

## Dirichlet mixtures of multinomials

- 08 January 2022
- advanced

This example notebook demonstrates the use of a Dirichlet mixture of multinomials (a.k.a Dirichlet-multinomial or DM) to model categorical count data. Models like this one are important in a variety of areas, including natural language processing, ecology, bioinformatics, and more.

## Bayesian Estimation Supersedes the T-Test

- 07 January 2022
- beginner

Non-consecutive header level increase; H1 to H3 [myst.header]

## Using a “black box” likelihood function (numpy)

- 16 December 2021
- beginner

This notebook in part of a set of two twin notebooks that perform the exact same task, this one uses numpy whereas this other one uses Cython

## Marginalized Gaussian Mixture Model

- 18 September 2021
- intermediate

Gaussian mixtures are a flexible class of models for data that exhibits subpopulation heterogeneity. A toy example of such a data set is shown below.

## Dirichlet process mixtures for density estimation

- 16 September 2021
- advanced

The Dirichlet process is a flexible probability distribution over the space of distributions. Most generally, a probability distribution, \(P\), on a set \(\Omega\) is a [measure](https://en.wikipedia.org/wiki/Measure_(mathematics%29) that assigns measure one to the entire space (\(P(\Omega) = 1\)). A Dirichlet process \(P \sim \textrm{DP}(\alpha, P_0)\) is a measure that has the property that, for every finite disjoint partition \(S_1, \ldots, S_n\) of \(\Omega\),