Use entirely open models and open source tools to automatically generate videos of any length you want!

In this post we walk through the architecture of diffusion models and show how to build one using only linear models as components!

In this post we see if GPT is powerful enough to be able to accurately predict the winner of a headline A/B test! Along the way we explore multiple approaches an modeling languages and learn how to build a model that can predict the difference between two vectors.

In this post we take a look a how the mathematical idea of a convolution is used in probability. In probability a convolution is a way to add two random variables. Using a slightly ridiculous, mad-science, example we walk through multiple way to compute a convolution and ultimate arrive at the formula with a better understanding of this powerful concept!

In this post we explore how we can use the Black-Scholes Merton model, together with the volatility smile of real options prices to determine the probability that Elon Musk will successfully purchase Twitter.

In this post we explore using censored data for an estimation problem. Our example is 100 scientist asked if they believe the weather at a time in the future will be lower or higher than specified number. We end up with a continuous distribution representing the beliefs of the scientists.

In this post we take a deep dive exploring the topic of Modern Portfolio Theory. We slowly walk through modeling a single stock, multiple correlated stocks and finally optimizing our portfolio. We’ll be making use of JAX to build our model with allows for easy optimization with the techniques of differentiable programming.

In this post we use Bayesian reasoning to understand the media, specifically an article about politically motivated interstate moving in the US as written about by NPR. We see that an important part of Bayesian reasoning is that our beliefs are never fixed.