This course covers the EM Algorithm and its applications in statistics. By the end of the course, learners will be able to understand the theory behind the EM Algorithm, apply it to various examples such as multinomial distributions, linear regression, and bivariate distributions, and calculate parameters like mean and variance for different probability distributions. The teaching method includes theoretical explanations followed by practical examples. This course is intended for individuals interested in statistics, data analysis, and probabilistic modeling.
Overview
Syllabus
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.
Taught by
statisticsmatt
