[MODELING WEBINAR] Bayesian Additive Regression Trees (BARTs), with Sameer Deshpande

What is this webinar about
Join us for an informative webinar that delves into the fascinating world of Bayesian Additive Regression Trees (BART). Designed for data analysts, scientists, and machine learning enthusiasts, this session will provide an overview of BART and its applications.
Whether you are new to BART or already have some experience, this webinar will provide valuable insights and actionable knowledge to enhance your data analysis and predictive modeling capabilities.
What are BARTs?
Bayesian Additive Regression Trees offer a flexible and interpretable framework for predictive modeling, combining the strengths of Bayesian statistics and ensemble learning.
By integrating decision trees and Bayesian inference, BART can effectively handle complex and high-dimensional datasets, capturing intricate interactions and nonlinear relationships that traditional methods might miss.
During this webinar, Sameer Deshpande will guide you through the fundamentals of BART and demonstrate its unique capabilities.
Who the expert is
Sameer is an assistant professor of Statistics at the University of Wisconsin-Madison. Prior to that, he completed a postdoc at MIT and earned his Ph.D. in Statistics from UPenn.
On the methodological front, he is interested in Bayesian hierarchical modeling, regression trees, model selection, and causal inference.
Much of his applied work is motivated by an interest in understanding the long-term health consequences of playing American-style tackle football.
He also enjoys modeling sports data and was a finalist in the 2019 NFL Big Data Bowl.
Outside of Statistics, he enjoys cooking, making cocktails, and photography - sometimes doing all of those at the same time…
Be among the first to know
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Useful references:
Dive into BARTs and Sameer's research: learnbayesstat...
Follow Learning Bayesian Statistics: learnbayesstat...
Support the show on Patreon: / learnbayesstats
Alex on Twitter: / alex_andorra
Alex on LinkedIn: / aandorra-pollsposition
Sameer’s website: skdeshpande91....
Sameer on Twitter: / skdeshpande91
Sameer’s own re-implementation of BART: github.com/skd...

[@araldjean-charles3924]

Great explanation. Has anyone ever thought of using these ideas for a language model? It could have continuous learning built in, due to the Bayesian Approach.