AM 226: Introduction to Stochastic Control Theory 

Tues., Thurs., 10:30-11:50 a.m., Room: B&H 160  

  1. Introduction: Applications and Elementary Examples
  2. Theory of Controlled Markov Chains-Dynamic Programming Equations
  3. Continuous Time Models-Controlled Diffusions
  4. Standard Formulations of Stochastic Control
  5. Connections with PDE
  6. Approximation of Processes-Examples and General Methods
  7. Dynamic Programming Equations for Markov Chains-Methods of Solution
  8. Weak Convergence
  9. Convergence Proofs
  10. Special Topics (E.g., Reflected or Jump Processes)