A Visual Introduction to Fisher Information and the Cramér-Rao Lower Bound

This video provides a formal and concise introduction to the statistical concepts of Fisher Information and the Cramér-Rao Lower Bound (CRLB). It is intended for students and researchers in physics, engineering, data science, and other quantitative fields seeking a clear understanding of the principles of parameter estimation.

Through a series of animated illustrations, this tutorial explains the fundamental theory of statistical inference. We begin with the concept of Maximum Likelihood Estimation (MLE) and build an intuitive yet rigorous understanding of Fisher Information as a measure of the curvature of the log-likelihood function. The presentation demonstrates how information from independent measurements accumulates and sets a fundamental limit on measurement precision, as defined by the Cramér-Rao Lower Bound. The tutorial concludes by showing how these principles are applied to practical experimental design, including multi-parameter estimation with the Fisher Information Matrix (FIM) and advanced optimization strategies.

Source Code for the slides and figures: https://github.com/Duxon/visual_intro...
License: MIT License
Audio generated via: Chatterbox AI (https://github.com/resemble-ai/chatte...)