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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 51 - Swarm Lifelogging, Microsoft Evolving, and Sports Analyzing with Avneesh Kohli
Jan 28

Jan 28 Episode 51 - Swarm Lifelogging, Microsoft Evolving, and Sports Analyzing with Avneesh Kohli

Max Sklar
Guest Interview
Episode 50 - Gatekeepers Bypassed, Anonymous Tips, and Facebook's Familiar Frustrations
Jan 21

Jan 21 Episode 50 - Gatekeepers Bypassed, Anonymous Tips, and Facebook's Familiar Frustrations

Max Sklar
Cohost Discussion
Episode 49 - Happier Maps, Better Routes, and the Tyranny of Efficiency with Daniele Quercia
Jan 14

Jan 14 Episode 49 - Happier Maps, Better Routes, and the Tyranny of Efficiency with Daniele Quercia

Max Sklar
Guest Interview
Episode 48 - Chasing the Perfect Voting System with Daniel Kronovet
Jan 7

Jan 7 Episode 48 - Chasing the Perfect Voting System with Daniel Kronovet

Max Sklar
Guest Interview

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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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