Learning Models and Inference · free preview

Parameter Estimation

Parameter Estimation

1. Parameter Estimation

Parameter Estimation

2. Parameter Estimation (2)

Parameter Estimation (2)

3. Parameter Estimation (3)

Parameter Estimation (3)

4. Closed-form least squares

The least-squares solution of Xθ=yX\theta = y (with XX of full column rank) is θ=\theta = ______ .

5. Maximum likelihood under Gaussian noise

For linear regression with Gaussian noise of fixed variance, maximum likelihood is equivalent to

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