Linear Models

The lm.py module implements:

1. OLS linear regression with maximum likelihood parameter estimates via the normal equation.
2. Ridge regression / Tikhonov regularization with maximum likelihood parameter estimates via the normal equation.
3. Logistic regression with maximum likelihood parameter estimates via gradient descent.
4. Bayesian linear regression with maximum a posteriori parameter estimates via conjugacy
   • Known coefficient prior mean and known error variance
   • Known coefficient prior mean and unknown error variance

Plots