Linear Regression is a method for modelling a relationship between a dependent variable and independent variables. These models can be fit with numerous approaches. The most common is least squares, where we minimize the mean square error between the predicted values $\hat{y} = \textbf{X}\hat{\beta}$ and actual values $y$: $\left(y-\textbf{X}\beta\right)^{2}$.
We can also define the problem in probabilistic terms as a generalized linear model (GLM) where the pdf is a Gaussian distribution, and then perform maximum likelihood estimation to estimate $\hat{\beta}$.
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In-Context Learning | 18 | 5.49% |
Decision Making | 11 | 3.35% |
Federated Learning | 10 | 3.05% |
Time Series Analysis | 10 | 3.05% |
Continual Learning | 9 | 2.74% |
Uncertainty Quantification | 9 | 2.74% |
Management | 9 | 2.74% |
Variable Selection | 9 | 2.74% |
Causal Inference | 8 | 2.44% |
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