Posts tagged BART
Modeling Heteroscedasticity with BART
In this notebook we show how to use BART to model heteroscedasticity as described in Section 4.1 of pymc-bart’s paper [Quiroga et al., 2022]. We use the marketing data set provided by the R package datarium [Kassambara, 2019]. The idea is to model a marketing channel contribution to sales as a function of budget.
Quantile Regression with BART
- 25 January 2023
- intermediate, explanation
Usually when doing regression we model the conditional mean of some distribution. Common cases are a Normal distribution for continuous unbounded responses, a Poisson distribution for count data, etc.
Bayesian Additive Regression Trees: Introduction
- 21 December 2021
- intermediate, explanation
Bayesian additive regression trees (BART) is a non-parametric regression approach. If we have some covariates \(X\) and we want to use them to model \(Y\), a BART model (omitting the priors) can be represented as: