Lecture
This lecture covers the application of Bayesian methods in choice modeling, focusing on personalized menu optimization. It discusses the use of Gibbs sampling with embedded Metropolis-Hastings for parameter estimation, the importance of individual-specific optimization, and the prediction of individual choices. The lecture also delves into the formulation and optimization of menus to maximize consumer surplus, social welfare, and revenue. A case study on the choice of grapes is presented, showcasing parameter estimation and population sample analysis. Additional readings and practical applications, such as the Tripod app for sustainable travel incentives, are also discussed.