The lm.py module implements:
- OLS linear regression with maximum likelihood parameter estimates via the normal equation.
- Ridge regression / Tikhonov regularization with maximum likelihood parameter estimates via the normal equation.
- Logistic regression with maximum likelihood parameter estimates via gradient descent.
- 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
