Expectation Maximization (EM) - 1 - Theory

Become a member! https://meerkatstatistics.com/courses/ * 🎉 Special YouTube 60% Discount on Yearly Plan – valid for the 1st 100 subscribers; Voucher code: First100 🎉 * Why become a member? * All video content * Extra material on complete-courses (notebooks) * Access to code and notes * Community Discussion * No Ads * Support the Creator ❤️ If you’re looking for statistical consultation, work on interesting projects, or training workshop, visit my website https://meerkatstatistics.com/ or contact me directly at [email protected] ~~~~~ SUPPORT ~~~~~ Paypal me: https://paypal.me/MeerkatStatistics ~~~~~~~~~~~~~~~~~ The EM algorithm is used to estimate model parameters when there are missing data, or latent variables. It overcomes the problem by using an iterative method to maximize the complete-likelihood (e.g., the likelihood of both the observed and unobserved data). In this video I show the theory behind the algorithm. Part 2: https://www.youtube.com/watch?v=J24CifymPbo ~~~~~ SUPPORT ~~~~~ Paypal me: https://paypal.me/MeerkatStatistics ~~~~~~~~~~~~~~~~~ Intro/Outro Music: Dreamer - by Johny Grimes https://www.youtube.com/watch?v=CACgsYjeK54