Parameter Estimation and Fitting Distributions

Learn parameter estimation in statistics through a practical 24-minute video tutorial that demonstrates both classic and modern machine learning approaches using radioactive decay as a foundational example. Explore the fundamental concepts of parameter estimation starting with traditional statistical methods before transitioning to contemporary ML techniques. Follow along with a hands-on code demonstration analyzing Americium 241 decay data to see parameter estimation applied in practice. Examine key distribution fitting questions and understand the mathematical formulation of parameter estimation problems. Master two essential estimation methods: Maximum Likelihood Estimation for finding parameters that best explain observed data, and the Method of Moments for parameter estimation using sample statistics. Gain practical insights into when and how to apply these different approaches for fitting distributions to real-world data sets.