Maximum Likelihood Inference

This lecture covers the concept of maximum likelihood inference, where models are compared based on the likelihood ratio. Examples are provided to illustrate the process, such as comparing models using the likelihood or the likelihood ratio. The lecture also delves into Bayesian inference, explicitly comparing the two approaches. The instructor discusses the inference process using a coin example, demonstrating how probability calculations are made based on observed outcomes. The lecture progresses to discuss model selection, parameter estimation, and the importance of maximizing the likelihood function to infer probability distributions from data.