Markov Chains: Transition Probabilities

Explores diffusion models, focusing on generating samples from a distribution and the importance of denoising in the process.

Covers applied probability, stochastic processes, Markov chains, rejection sampling, and Bayesian inference methods.

Explores invariant distributions for Markov Chains, emphasizing uniqueness and implications in communicating classes.

Explores Bourgain's theorem on sparsest cut in graphs, emphasizing semimetrics and cut optimization.

Covers concentration inequalities and sampling methods for estimating unknown distributions, with a focus on population infection rates.