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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 147 - Joining Locals, Media Decline, and Digital Detox
Nov 30

Nov 30 Episode 147 - Joining Locals, Media Decline, and Digital Detox

Max Sklar
Cohost Discussion
Episode 146 - Math, Language, and Intelligence with Tai-Danae Bradley
Nov 23

Nov 23 Episode 146 - Math, Language, and Intelligence with Tai-Danae Bradley

Max Sklar
Guest Interview
Episode 145 - What Is Probability? A Philosophical Question with Practical Implications
Nov 16

Nov 16 Episode 145 - What Is Probability? A Philosophical Question with Practical Implications

Max Sklar
Recorded Talk
Episode 144 - Brian Mac Mahon of Expert DOJO on Startups
Nov 9

Nov 9 Episode 144 - Brian Mac Mahon of Expert DOJO on Startups

Max Sklar
Guest Interview
Episode 143 - One Last Episode Before the US Presidential Election
Nov 2

Nov 2 Episode 143 - One Last Episode Before the US Presidential Election

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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