Course Content

  • 2.8.1c Distribution Models

Course Content


Data with a Normal Distribution is useful for model construction in Machine Learning. It simplifies math. Models like LDA, Gaussian Naive Bayes, Logistic Regression, Linear Regression, etc., are explicitly calculated from the assumption that the distribution is a bivariate or multivariate normal.

Probability distributions are classified in a variety of ways. The normal distribution, chi square distribution, binomial distribution, and Poisson distribution are a few examples. The various probability distributions serve distinct functions and represent distinct data creation processes.

This bell-shaped curve is known as the Normal Distribution. Because Carl Friedrich Gauss discovered it, we sometimes refer to it as a Gaussian Distribution. We may reduce the Normal Distribution's Probability Density to two parameters: 𝝻 Mean and 𝛔2. This curve is symmetric around the Mean.

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