# Posts tagged hierarchical model

## Faster Sampling with JAX and Numba

- 11 July 2023

PyMC can compile its models to various execution backends through PyTensor, including:

## Hierarchical Partial Pooling

- 28 January 2023

Suppose you are tasked with estimating baseball batting skills for several players. One such performance metric is batting average. Since players play a different number of games and bat in different positions in the order, each player has a different number of at-bats. However, you want to estimate the skill of all players, including those with a relatively small number of batting opportunities.

## Hierarchical Binomial Model: Rat Tumor Example

- 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.

## Bayesian Vector Autoregressive Models

- 26 November 2022

Duplicate implicit target name: “bayesian vector autoregressive models”.

## A Primer on Bayesian Methods for Multilevel Modeling

- 24 October 2022

Hierarchical or multilevel modeling is a generalization of regression modeling.

## NBA Foul Analysis with Item Response Theory

- 17 April 2022

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.

## A Hierarchical model for Rugby prediction

- 19 March 2022

In this example, we’re going to reproduce the first model described in Baio and Blangiardo [2010] using PyMC. Then show how to sample from the posterior predictive to simulate championship outcomes from the scored goals which are the modeled quantities.

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

- 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.

## Diagnosing Biased Inference with Divergences

- 26 February 2018

This notebook is a PyMC3 port of Michael Betancourt’s post on mc-stan. For detailed explanation of the underlying mechanism please check the original post, Diagnosing Biased Inference with Divergences and Betancourt’s excellent paper, A Conceptual Introduction to Hamiltonian Monte Carlo.