Bayesian Regression and Model Selection

About this course

Master Bayesian modeling through Bayesian linear regression, generalized linear models, hierarchical models and model selection. This course will deepen your understanding of modeling techniques and the importance of the prior when contrasted with traditional frequentist modeling approaches. You will understand the benefits of hierarchical models and how they automatically identify the right amount of pooling between data to provide a balance between the complete and no pooling approaches. You will learn how to apply posterior predictive checks for model selection and understand the Occam’s razor principle. This course combines theoretical modeling foundations with hands-on implementations.

What you will learn

• Implement variational inference for scalable Bayesian analysis and determine when to prefer VI over MCMC methods.
• Apply Gaussian Process Regression and Dirichlet Processes for flexible non-parametric modeling solutions.
• Execute complete Bayesian workflows using PyMC3 from model specification through validation and diagnostics.
• Build decision-theoretic models using loss functions for applications in sports analytics, healthcare, and business decision-making.