Nonparametric and Bayesian Statistics

This lecture covers the principles of nonparametric statistics, focusing on estimating parameters without assuming a specific model. It discusses plug-in estimation, kernel density estimation, and the trade-off between bias and variance. The lecture also delves into Bayesian statistics, explaining the concept of posterior distribution, credible intervals, and the highest posterior density region. Practical approaches for selecting bandwidth parameters and summarizing posterior distributions are explored.