mathprobabilityfoundations
Bayesian inference
Priors, posteriors, conjugacy, MCMC and variational inference — principled uncertainty quantification in models.
Уровни глубины
L0Intro~3ч
Reads Bayes' rule; distinguishes frequentist vs Bayesian perspective.
L1Basics~15ч
Uses conjugate priors (Beta-Binomial, Normal-Normal); derives posterior updates analytically.
L2Working~30ч
Runs PyMC/Stan models; applies MCMC (HMC/NUTS) and VI; builds Bayesian linear/logistic regression.
L3Advanced~40ч
Implements normalising flows; analyses posterior approximation error; Bayesian neural networks.
L4Research~80ч
Contributes to scalable Bayesian deep learning or variational methods.
Ресурсы
L1 — Basics
L2 — Working