V7 Bayesian Curve Fitting | Maximum Likelihood and Maximum Posterior Estimation
In this video, we discuss the Bayesian perspective of the polynomial curve fitting problem. We will also discuss parameter estimation using maximum likelihood and maximum posterior methods, and draw parallels between the least square estimation and the regularized least square estimation, respectively, under the Gaussian error distribution.
Previous video on Maximum Likelihood Estimation:
• V5 Probability Density Function | Gaussian...
Code for Bayesian Curve Fitting
Video: • V8 Bayesian Curve Fitting in Python
Jupyter Notebook: https://github.com/ruchikaverma-iitg/...
Github link for our ML lectures and codes-
https://github.com/ruchikaverma-iitg/...