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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 38 - Predictions Panel on Virtual Services, Rise of the Outer Suburbs, and Zombie Coins
Oct 29

Oct 29 Episode 38 - Predictions Panel on Virtual Services, Rise of the Outer Suburbs, and Zombie Coins

Max Sklar
Expert Panel
Episode 37 - Naomi Brockwell on Blockchain Tech and Producing for YouTube and TV
Oct 23

Oct 23 Episode 37 - Naomi Brockwell on Blockchain Tech and Producing for YouTube and TV

Max Sklar
Guest Interview
Episode 36 - The Google Graveyard
Oct 15

Oct 15 Episode 36 - The Google Graveyard

Max Sklar
Cohost Discussion
Episode 35 - Camille Fournier and the Manager's Path
Oct 9

Oct 9 Episode 35 - Camille Fournier and the Manager's Path

Max Sklar
Guest Interview
Episode 34 - Data Engineering with Joe Crobak, Foundations of Smart Software
Oct 2

Oct 2 Episode 34 - Data Engineering with Joe Crobak, Foundations of Smart Software

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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