Statistical estimation algorithms for fitting models to observed data and making predictions.
| Algorithm | Description |
|---|---|
| Linear Regression | Ordinary least-squares regression using the normal equation |
| Polynomial Fitting | Degree-d polynomial fit via Vandermonde normal equations |
| Total Least Squares | Errors-in-variables fit (noisy regressors) via the SVD of `[A |
| Yule-Walker | Autoregressive model parameter estimation via the Yule-Walker equations |
| Expectation-Maximization | EM algorithm for Kalman filter parameter identification (Shumway-Stoffer) |
| Incremental QR Least Squares | Streaming Givens QR in fixed memory with a rank-revealing SVD solve (minimum-norm, identifiable combinations) |
| Algorithm | Description |
|---|---|
| Recursive Least Squares | Sample-by-sample parameter estimation with exponential forgetting factor |
| Recursive Instrumental Variables | Recursive least squares that stays unbiased when the regressor is measured with noise |