Roadmap to Section 3.5 Evidence Approximation - Pattern Recognition and Machine Learning

In this video, we give a road map to this advanced section, why it's worth learning, and what we will learn in each of the subsections, which introduce and evaluate the evidence function, show how to maximize it and define the effective number of parameters that are learned from the data, respectively. My notes: https://sinatootoonian.com/index.php/2024/12/30/notes-on-the-evidence-approximation/ MacKay's paper: https://direct.mit.edu/neco/article-abstract/4/3/415/5639/Bayesian-Interpolation Erik Bekkers' video: https://www.youtube.com/watch?v=kBt9hPNxpI8