Introduction to Bayesian Linear Regression statistics | NerdML
Bayesian Linear Regression - This video will help you to understand the drawbacks of solving Linear Regression problems using Least Square Error or Gradient Descent over Statistical approach of Bayesian Learning. I address the question of why a Bayesian approach is preferable to using the MLE or MAP estimate.
We use a coin toss experiment to demonstrate the idea of prior probability, likelihood functions, posterior probabilities, posterior means and probabilities as well as credible intervals. Derived mathematical functions of Bayesian learning.
Below topics are explained in this video:
1. Frequentist vs Bayesian approach? ( 00:15 )
2. Case Study on Frequentist approach ( 00:55 )
3. Bayesian Approach over Frequentist Method for solving Linear Regression problems ( 05:07 )
4. Why we need to use Bayesian Learning for solving Linear Regression problem (05:49)
5. Linear Regression solution using Bayesian Learning (08:14)
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Please find the previous Video link - An Introduction to Simple Linear Regression Analysis - Gradient Descent | NerdML : • An Introduction to Simple Linear Regressio...
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Prerequisites
Basic understanding of Linear Algebra, Probability, Matrix & Python programming including pandas, numpy, scikit learn & some visualization tools.
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Creator : Rahul Saini
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