#bayes
3 articles
01
·Probability & Statistics·★ MEMBER·11 min read
Thinking Bayesian — A Working Feel for Priors, Likelihoods, and Posteriors
Bayesian updating is the act of feeding yesterday's posterior back in as today's prior. With a conjugate prior the whole update collapses into adding pseudo-counts, and an A/B test becomes two numbers: the probability of winning and the expected loss. From zero background to the traps in stopping rules and prior choice.
02
·Probability & Statistics·★ MEMBER·PAPER·10 min read
Bayes' Theorem in AI — Priors, Posteriors, and Uncertainty
One line of math — Bayes' theorem — turns into three workhorse tools in real AI systems: probability calibration, active learning, and Bayesian optimization. Starting from a positive medical test, we build up priors, posteriors, and uncertainty with zero background assumed.
03
·Probability & Statistics·★ MEMBER·8 min read
Probability and Statistics for AI — A Model's Output Is a Distribution
Classifiers and language models do not return answers; they return probability distributions. Distributions, expectation, conditional probability and Bayes explained from the symbols up — building to the payoff: why maximum likelihood is where loss functions come from. Cross-entropy and MSE were derived, not invented.