Statistics
π(θ) Bayesian Statistics
Treating unknowns as probability distributions and updating beliefs with data — from Bayes' theorem through modern computational methods.
3 concepts— start at the top and work your way down
- 1→
Markov Chain Monte Carlo
Sampling from a posterior distribution you can't write down in closed form, by building a random walk whose long-run behavior matches that distribution.
- 2→
INLA
Integrated Nested Laplace Approximation — a fast, deterministic alternative to MCMC for a broad class of Bayesian models, trading some generality for speed and reliable convergence.
- 3→
Naive Bayes
A probabilistic classifier that applies Bayes' theorem with the (often unrealistic) assumption that features are conditionally independent given the class.