Posts tagged hierarchical model
Faster Sampling with JAX and Numba
- 11 July 2023
- intermediate, reference
PyMC can compile its models to various execution backends through PyTensor, including:
Hierarchical Partial Pooling
- 28 January 2023
- intermediate
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
- 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.
Bayesian Vector Autoregressive Models
- 28 November 2022
- intermediate
Duplicate implicit target name: “bayesian vector autoregressive models”.
A Primer on Bayesian Methods for Multilevel Modeling
- 24 October 2022
- intermediate
Hierarchical or multilevel modeling is a generalization of regression modeling.
NBA Foul Analysis with Item Response Theory
- 17 April 2022
- intermediate, tutorial
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
- intermediate, how-to
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
- 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.
Diagnosing Biased Inference with Divergences
- 28 February 2018
- intermediate
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.