Maximum Likelihood Estimation | Gaussian Normal Distribution (Estimating the Mean μ and Variance σ)

This video, gives a complete explanation to the Estimation of the Mean μ and Variance σ of Gaussian (Normal) Noise Distribution using Maximum Likelihood Estimation (MLE). In this video you'll learn about Maximum Likelihood Estimation and how it can be applied to estimate the mean μ and variance σ of a Gaussian (Normal) noise distribution. I break down the process of estimating the mean μ and variance σ of a Gaussian Distribution step-by-step, demonstrating how to derive the Maximum Likelihood Estimation for both parameters.

Whether you're studying statistics, signal processing, or machine learning, understanding how Maximum Likelihood Estimation works with Gaussian noise is essential for a wide range of applications, including noise filtering, data fitting, and model parameter estimation. This video is for you!

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