Learning

Mathematical Foundations for Theoretical Statistics and Advanced Inference

Multivariable Calculus → Bayesian Inference

This learning path builds a rigorous mathematical foundation for theoretical statistics and advanced inference. It covers essential calculus, linear algebra, and probability, then progresses through measure-theoretic probability, distribution theory, and the core pillars of inference: estimation, hypothesis testing, likelihood methods, and asymptotic theory. The path is designed for university students in statistics, mathematics, or data science who seek a systematic and deep understanding.