An explanation of Bayes' theorem visually using a geometric diagram to show how new evidence updates probability beliefs.
Perhaps the most important formula in probability.
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An equally valuable form of support is to simply share some of the videos.
Special thanks to these supporters: 3b1b.co/bayes-thanks
Home page: 3blue1brown.com
Here's an interactive version of the main diagram one viewer made:
srulix.com/projects/bayesground?standalone=true
The quick proof: youtu.be/U_85TaXbeIo
Interactive made by Reddit user Thoggalluth: nskobelevs.github.io/p5js/BayesTheorem/
The study with Steve:
science.sciencemag.org/content/185/4157/1124its.caltech.edu/~camerer/Ec101/JudgementUncertainty.pdf
You can read more about Kahneman and Tversky's work in Thinking Fast and Slow, or in one of my favorite books, The Undoing Project.
Contents:
- Intro example
- Generalizing as a formula
- Making probability intuitive
- Issues with the Steve example
Thanks to these viewers for their contributions to translations
Gujarati: Hitesh12358, Pragna1991
Hebrew: Omer Tuchfeld
Italian: @crampaldo
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These animations are largely made using manim, a scrappy open-source python library: github.com/3b1b/manim
If you want to check it out, I feel compelled to warn you that it's not the most well-documented tool, and it has many other quirks you might expect in a library someone wrote with only their own use in mind.
Music by Vincent Rubinetti.
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open.spotify.com/album/1dVyjwS8FBqXhRunaG5W5u
If you want to contribute translated subtitles or to help review those that have already been made by others and need approval, you can click the gear icon in the video and go to subtitles/cc, then "add subtitles/cc". I really appreciate those who do this, as it helps make the lessons accessible to more people.
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Why it's here
2 people recommend this
What people say. Highly recommended for providing an intuitive, visual understanding of Bayes' theorem and conditional probability.
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godelskion Hacker News • Recommends
On “Instrumenting Python GIL with eBPF”
Probably, but more likely a lack of knowledge and an overestimation of their knowledge. People are famously bad with statistics but people are also famously over confident in their understanding of numbers and data. I mean cloud servers really try to get some extra 9s on the end of their uptime. (People are also __terrible__ with rates of change. e.g. 90% increase as from last year). But I think an interesting one is the also literally the most common example that's in many stats textbooks, at least the ones that cover Bayesian stats. That being that if a test is 95% accurate, that it does not mean that that scoring positive on a test means that there's a 95% chance you have whatever the tes…
On “Home schooling's rise from fringe to fastest-growing form of education”
This is the classic problem solved by Bayes: link to this video there just aren't that many homeschooled people to provide a defense. I'm another successful homeschooled-until-college adult, but homeschooling isn't that common now and it was even less popular when I was growing up. EDIT: unless you were mentioning defense of homeschooling given they were homeschooled. I would assume most people on HN who were homeschooled had a decent education/homeschooling experience.
> This metric is also a proxy for living in a violent environment. Probably, probably not. The probabilities of witnessing someone being shot is extremely low in both environments. If amount of people who are living in violent environment is much lower, it may be that a person who witnessed someone being shot is more probable from a good environment. linklink to this video
