Count Bayesie - A Probability Blog

As a writer, I’m frequently disappointed with the quality, and in particularly the feel, of LLM writing.

In this post we’ll learn how to create arbitrary length, continuous one-shot videos in an entirely automated fashion using only open models and open source tools by exploring my newest project WeirdDream which is tool for generating content for a TikTok c…

It seems like everyone is releasing some cool model right now, so why not join the fun?

A good chunk of my career has involved running, analyzing and writing about A/B tests (here’s a quick Bayesian overview of A/B testing if you aren’t familiar).

After watching the recent 3Blue1Brown video on convolutions I realized that there is a surprising lack of articles on convolutions as they apply to probability.

As you are probably aware Elon Musk has long been wavering in his decision whether or not to purchase Twitter (ticker: TWTR) for a price that would amount to $54.20 a share.

The other day I received a really interesting inference question from @stevengfan8 on Twitter.

In this post we're going to take a deep dive into understanding Modern Portfolio Theory, a strategy for optimizing assets in an investment portfolio to balance the rate of risk and reward.

One of the, somewhat odd, morning rituals I've developed is what I would call Bayesian media analysis.

A few years back I used to mentor data scientists in a bootcamp.

Statistics is, at its heart, the study of how little you actually know about anything.

People (well the tiny subset of them deeply and passionately interested in statistics) tend to argue a lot about the difference between "Bayesian" and "Frequentist" approaches to solving problems.

In part one of this series we built out a simple neural network (perceptron) to model the problem of Click Through Rate (CTR) for a job application website.

This post is the first in a three part series covering the difference between prediction and inference in modeling data.

There is no shortage of posts on Survivorship Bias out there, so why read yet another one?

Thompson sampling is an optimization technique frequently used to solve multi-armed bandit problems, where we choose an action probabilistically based on the probability of it offering the highest reward.

This post emerged from a series of question surrounding a Twitter comment that brought up some very interesting points about how Bayesian Hypothesis testing works and the inability of analytic solutions to solve even some seemingly trivial problems in Bay…

The audience of this blog is largely academics, technologists and data scientists working in the tech industry.

In November 2019 I gave an invited tutorial on Probability and Statistics at PyData NYC.

In the last post we looked at how we can derive logistic regression starting from Bayes' Theorem.