Posts tagged BART
Categorical regression
- 06 May 2024
In this example, we will model outcomes with more than two categories.
Bayesian Non-parametric Causal Inference
- 06 January 2024
There are few claims stronger than the assertion of a causal relationship and few claims more contestable. A naive world model - rich with tenuous connections and non-sequiter implications is characteristic of conspiracy theory and idiocy. On the other hand, a refined and detailed knowledge of cause and effect characterised by clear expectations, plausible connections and compelling counterfactuals, will steer you well through the buzzing, blooming confusion of the world.
Quantile Regression with BART
- 25 January 2023
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.
Modeling Heteroscedasticity with BART
- 06 January 2023
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.
Bayesian Additive Regression Trees: Introduction
- 21 December 2021
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: