Developing an intuition for probability using analogies from physics (mass distributions, centers of mass, resolution/granularity), exploring both the standard measure-theoretic and the expectation...

Uncovering the hidden algebra within probability, using indicators and expectation to convert sets and probabilities into arithmetic.

Introduction to Bregman projections in information geometry, exploration connections between basic loss functions and the mean as a predictor.

A beginner-friendly introduction to stochastic calculus, focusing on intuition and calculus-based derivations instead of heavy probability theory formalism.

Introduction Calculus can be quite tedious when computed symbolically by hand. In many modern applications (for example, in machine learning), automatic differentiation is used to efficiently comp...

A detailed derivation of the reverse-time stochastic differential equation used in Score-Based Generative Modeling.