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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
Episode 187 - Coding from Natural Language, NYT Tech Walkout, Off Predictions
Aug 30

Aug 30 Episode 187 - Coding from Natural Language, NYT Tech Walkout, Off Predictions

Max Sklar
Cohost Discussion
Episode 186 - Cubans embrace Bitcoin amidst Monetary Oppression, and the Last Jew in Afghanistan
Aug 23

Aug 23 Episode 186 - Cubans embrace Bitcoin amidst Monetary Oppression, and the Last Jew in Afghanistan

Max Sklar
Cohost Discussion
Episode 185 - Goldbacks in Your Pocket with Benjamin Shaffer
Aug 16

Aug 16 Episode 185 - Goldbacks in Your Pocket with Benjamin Shaffer

Max Sklar
Guest Interview
Episode 184 - Academic Freedom, Crypto Regs, and Security Devices
Aug 9

Aug 9 Episode 184 - Academic Freedom, Crypto Regs, and Security Devices

Max Sklar
Solo Show
Episode 183 - The Metaverse and Digits
Aug 2

Aug 2 Episode 183 - The Metaverse and Digits

Max Sklar
Cohost Discussion

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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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