SBAMDT is a Bayesian additive tree model with hard-or-soft and univariate-or-multivariate splits, reporting better spatial predictions than existing tree ensembles.
Bayesian Additive Regression Trees With Parametric Models of Heteroskedasticity
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abstract
We incorporate heteroskedasticity into Bayesian Additive Regression Trees (BART) by modeling the log of the error variance parameter as a linear function of prespecified covariates. Under this scheme, the Gibbs sampling procedure for the original sum-of- trees model is easily modified, and the parameters for the variance model are updated via a Metropolis-Hastings step. We demonstrate the promise of our approach by providing more appropriate posterior predictive intervals than homoskedastic BART in heteroskedastic settings and demonstrating the model's resistance to overfitting. Our implementation will be offered in an upcoming release of the R package bartMachine.
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SBAMDT: Bayesian Additive Decision Trees with Adaptive Soft Semi-multivariate Split Rules
SBAMDT is a Bayesian additive tree model with hard-or-soft and univariate-or-multivariate splits, reporting better spatial predictions than existing tree ensembles.