My thoughts and opinions for Bayesian Machine Learning.

I want to document my journey in Machine Learning where I stamp interesting frameworks and why I find them interesting. A reminder to self: Great ideas do not come from nowhere. They are derived from fundamental concepts, collaboration, and lots of trial and error.

31/10/2025: Bayesian Optimal Experimental Design (why Expected Information Gain?)

Description: I wanted to investigate the origin story for InfoBAX, have a short introduction on BOED. How and why the ideas of info-theoretic BO came around. Useful Resources:

• Bayesian Optimal Experimental Design notes: Desi Ivanova does a great simple introduction and visualization to BOED. Here: Desi’s 7 min Read
• InfoBAX: Willie Neiswanger does a great talk on InfoBAX I really loved. Here: Willie Neiswanger’s Presentation
• My thoughts on InfoBAX: InfoBAX_insights
• Relevant Concepts: Lindley’s EIG, Hennig’s Entropy Search, Krause’s Mutual Information Maximization, A Geneticist’s Approximate Bayesian Computation demonstration.