Controlled Stochastic Processes

This lecture by the instructor covers the topic of controlled stochastic processes, focusing on analyzing proper processes, asymptotic behavior, distribution, and passage times. The lecture delves into the descriptive and prescriptive tasks of controlling stochastic processes, with examples such as steering a car from A to B with minimum energy. Dynamic programming is introduced as a solution method, emphasizing the principle of optimality. The lecture concludes with the application of dynamic programming algorithms to solve the machine replacement problem, showcasing the backward induction process to determine the optimal policy.