Max Slater @thenumb.at · 02/08/2025We've seen how to define and apply Monte Carlo integration, but there's a whole world of techniques for reducing variance. Part five (thenumb.at/QMC) covers Quasi-Monte Carlo: negative correlation, stratified and adaptive sampling, and low-discrepancy sequences. 0469
Max Slater @thenumb.at · 19/04/2025Monte Carlo has many uses, but path tracing is one of my favorites. Part four (thenumb.at/Rendering/) explores how Monte Carlo integration is used to simulate light transport. 2406
Max Slater @thenumb.at · 12/04/2025Monte Carlo methods require randomly sampling complicated domains, which can be difficult in of itself. Part three (thenumb.at/Sampling/) discusses how to create samplers using rejection, inversion, and changes of coordinates. 37010
Max Slater @thenumb.at · 05/04/2025Monte Carlo integration lets us integrate high-dimensional functions exponentially faster than traditional methods! Part two (thenumb.at/Monte-Carlo/) explores how and why it works. 0225
Max Slater @thenumb.at · 29/03/2025I'm working on a series of posts about Monte Carlo methods! The first (thenumb.at/Probability) is a review/overview of continuous probability, including random variables, distributions, expectation, variance, probability bounds, and the Dirac delta. 04110
Max Slater @thenumb.at · 29/07/2023Functions are vectors! This perspective lets us apply the tools of linear algebra to computational problems from image and geometry processing to machine learning and light transport—and provides a natural explanation for Fourier series. Let's explore: thenumb.at/Functions-are-Vectors 161
