Covers Likelihood Ratio Tests, their optimality, and extensions in hypothesis testing, including Wilks' Theorem and the relationship with Confidence Intervals.
Explores Gaussian Mixture Models for data classification, focusing on denoising signals and estimating original data using likelihood and posteriori approaches.
