Lecture
This lecture covers the concept of maximum likelihood inference, focusing on model selection and parameter estimation. It delves into the process of inferring probability distributions from data, emphasizing the importance of choosing the right model. The lecture also explores the challenges of comparing different models using likelihood ratios and the implications of prior knowledge in Bayesian inference.