Posts tagged hierarchical model
PyMC can compile its models to various execution backends through PyTensor, including:
- 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.
- 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.
- 28 November 2022
Duplicate implicit target name: “bayesian vector autoregressive models”.
- 24 October 2022
Hierarchical or multilevel modeling is a generalization of regression modeling.
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.
In this example, we’re going to reproduce the first model described in Baio and Blangiardo  using PyMC. Then show how to sample from the posterior predictive to simulate championship outcomes from the scored goals which are the modeled quantities.
- 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.
- 28 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.