Linear Regression: Statistical Inference Perspective

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Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Covers linear regression, regularization, inverse problems, X-ray tomography, image reconstruction, data inference, and detector intensity.

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Explores linear regression fundamentals, emphasizing the importance of regularization techniques to enhance model performance.

Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.

Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.

Covers linear regression, regularization, and probabilistic models in generating labels.