Applied Stochastic Analysis

Catalog description

The focus of the course is to introduce the key ideas, tools, and computational methods for working with Markov chains and stochastic differential equations without requiring a measure-theoretic understanding of probability. All that is required is undergraduate-level experience in probability, advanced calculus, linear algebra, and some determination to use these topics all at once. As Markov chains and stochastic differential equations are used as models for reality across many disciplines (economics, physics, biology, etc.), it is valuable for any applied mathematician to have some proficiency in them. The course may also serve as a useful stepping stone for those seeking stronger intuition through example and computation before diving into a measure-theoretic treatment.