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What is Gaussian Models and EM Algorithm Explained about?

The document discusses the relationship between a continuous Gaussian variable Y and its linear dependence on another variable X, focusing on maximizing the conditional likelihood. It explains the process of minimizing the sum of squared errors in linear regression with Gaussian noise. Additionally, it touches on learning with complete data using Naive Bayes models and the EM algorithm for estimating hidden data parameters.

Author

aakash goel

Language

EN