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.
You'll of course have the chance to interact with our speakers and ask them live questions. To ensure the quality of these interactions, the number of spots is limited, so make sure to register now to secure yours.
If you're a Patron of the Learning Bayesian Statistics podcast, you'll get at least a 50% discount, and have the ability to submit your questions to be pre-chosen before the webinar.
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 โ sometimes doing all of those at the same timeโฆ
Sign up and interact
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.
Don't miss this opportunity to expand your machine learning toolkit and uncover the potential of Bayesian Additive Regression Trees.
Useful references