EM Algorithm

Dive into a comprehensive exploration of the Expectation-Maximization (EM) Algorithm through this extensive video series. Begin with theoretical foundations before progressing to practical examples, including multinomial distributions, coin flipping scenarios, and simple linear regression with right censoring. Explore advanced topics such as bivariate Poisson and Normal distributions, integrating bivariate Normal distributions, and life testing applications. Gain insights into related concepts like truncated Normal and Exponential densities. Enhance your understanding of this powerful statistical tool for parameter estimation in incomplete data scenarios.

Part 1a - EM Algorithm (Part 1 Theory, Part 2 Examples)..
Part 1b - EM Algorithm (Part 1 Theory, Part 2 Examples)..
Part 2a - EM Algorithm - Multinomial Example.
Part 2b - EM Algorithm - Flipping 2 coins.
Mean and Variance of Truncated Normal Density.
Part 2c - EM Algorithm - Simple linear regression with right censoring.
Part 2d - EM Algorithm - Bivariate Poisson Distribution.
Derivation of Bivariate Normal and the Conditional Distributions.
Integrating a Bivariate Normal Distribution.
Part 2e - EM Algorithm - Bivariate Normal Distribution.
Mean and Variance of Truncated Exponential Density.
Part 2f - EM Algorithm - Life Testing.