Topic
Bayes Theorem
3 episodes
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Overview: Causal Inference
We keep circling causal inference because the difference between correlation and cause is where a lot of AI gets tricked. We finally slow it down, build the intuition from observational data to interventions, and show why that oxygen-mask problem keeps showing up everywhere.
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Overview: Bayes' Theorem
We finally slow down on Bayes' Theorem, the belief-updating rule we keep smuggling into conversations about evals, spam filters, diagnosis, ranking, and calibration. We make it click through one package-sorting picture: evidence only matters against the pile it came from.
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Overview: Conditional Probability
We keep running into conditional probability anywhere we try to reason from partial evidence, so we finally sat down and made it the whole point. We’re breaking down P(A|B), why the denominator matters, and why this little idea quietly sits under a ton of AI behavior.