Explores safe mutation in functional programming through lazy evaluation, object invariants, and efficient caching functions.
Covers the essentials of probability, algebras, and conditional probability, including the Borel o-algebra and Poisson processes.
Explores controlled stochastic processes, focusing on analysis, behavior, and optimization, using dynamic programming to solve real-world problems.
Explores the generalization of Martingale Central Limit Theorem to sub- and supermartingales, discussing key properties and corollaries.
