Stochastic Modeling for Data-Driven Decision Making

3 credits · GSB

Many decision-making systems benefit from the use of stochastic models. These models help an algorithm or AI agent make sense of the world and understand how the impacts of various actions propagate. However, choosing the right model and refining it with data remains a daunting challenge. Placing emphasis on dynamics arising from the physical reality and business problems, we will examine core frameworks for stochastic modeling while illustrating their application through examples ranging from personalization and dynamic pricing to reinforcement learning and world models.