BlackHC's Adventures

Have you ever wondered why we treat data selection differently during training versus before training? [Read More]

This note explores a seemingly simple yet surprisingly profound example of how rational agents can diverge in their beliefs even when exposed to identical evidence. While the mathematical model we’ll examine is highly simplified, its core mechanism offers a potential lens through which to understand the complex dynamics of real-world... [Read More]

Why is the Bayesian model average (BMA) often hailed as the optimal choice for rational actors making predictions under uncertainty? Is this claim justified, and if so, what’s the underlying logic? [Read More]

In the first part of this two-part series on Function-Space Variational Inference (FSVI), we looked at the Data Processing Inequality (DPI). In this second part, we finally look at the relationship between FSVI, a method focusing on the Bayesian predictive posterior rather than the parameter space, and the DPI. We... [Read More]

In information theory, the data processing inequality (DPI) is a powerful concept. Informally, it tells us that processing data cannot increase the amount of contained information. In this two-part blog post, we will explore the DPI and its applications to function-space variational inference (FSVI). [Read More]