Explores the convergence of Langevin Monte Carlo algorithms under different growth rates and smoothness conditions, emphasizing fast convergence for a wide class of potentials.
Explores sufficient statistics, data compression, and their role in statistical inference, with examples like Bernoulli Trials and exponential families.
