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This serves as a quick cheat-sheet for solving small Bayesian problems using a table.

Based in Sydney, Australia, Foundry is a blog by Rebecca Thao. Her posts explore modern architecture through photos and quotes by influential architects, engineers, and artists.
The Local Maximum Expand Your Perspective

Aug 25
Aug 25 What is a probability distribution?

Max Sklar

Dec 24
Dec 24 What is a magnitude space?

Max Sklar

Nov 28
Nov 28 How can there be a fraction of a bit?

Max Sklar

Nov 27
Nov 27 What is a bit?

Max Sklar

Nov 24
Nov 24 What is an argument from analogy?

Max Sklar

Nov 23
Nov 23 What is the regularity assumption?

Max Sklar

Oct 28
Oct 28 What is Occam's Razor?

Max Sklar

Sep 9
Sep 9 What is a probability simplex?

Max Sklar

Sep 5
Sep 5 What is a categorical distribution?

Max Sklar

Aug 28
Aug 28 What is topology?

Max Sklar

Aug 24
Aug 24 What is machine learning?

Max Sklar

Aug 24
Aug 24 What is underfitting?

Max Sklar

Aug 18
Aug 18 What is overfitting?

Max Sklar

Aug 13
Aug 13 What is a mathematical space?

Max Sklar

Aug 10
Aug 10 What is a hill climbing algorithm?

Max Sklar

Aug 6
Aug 6 What is a local maximum?

Max Sklar

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Subscribe on iTunes | Soundcloud | Stitcher | Odysee | Locals

Get Max's Free Cheat-Sheet on Bayesian Inference

Just sign up for the email list to keep up with exciting developments, floating ideas, and new projects from Max and his guests.

Thank you for joining The Local Maximum email list!

Download your free Bayesian Inference Page

This serves as a quick cheat-sheet for solving small Bayesian problems using a table.

The “Local Maximum” is a mathematical term referring to a point at which one needs to step down in order to reach new heights. But people - not just points - can get caught in a local maximum. That's what happens when we've gone as far as we can through one strategy which has gone stale and need to search for new ideas. This podcast is about examining technology, engineering, and social trends by expanding perspectives and moving beyond the Local Maximum both for ourselves and our algorithms.

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