Ohio State University

Heteroscedastic BART Using Multiplicative Regression Trees

Matthew Pratola, Assistant Professor of Statistics, The Ohio State University

May 7, 16:00 - 17:00

B1 L4 R4102

Ohio State University Environmental Statistics

Bayesian additive regression trees (BART) has become increasingly popular as a flexible and scalable non-parametric model useful in many modern applied statistics regression problems. It brings many advantages to the practitioner dealing with large and complex non-linear response surfaces, such as a matrix-free formulation and the lack of a requirement to specify a regression basis a priori.