Tutorials
Read these tutorials in order. The first six cover interpolation, fitting, and constraints; the final two explain the low-level BSpline and TensorSpline APIs.
Available Tutorials
- Spline Interpolation Interpolate exact data in one dimension and on rectilinear grids, and compare the results with MATLAB’s griddedInterpolant.
- Fitting Noisy Data Fit a smooth spline to noisy observations with a normal noise model and explore the effect of spline complexity.
- Robust Fitting with Outliers Replace a normal noise model with a Student-t model when a few observations are badly contaminated.
- Local Point Constraints Apply value, slope, and curvature constraints at specific points in a one-dimensional spline fit.
- Global Shape Constraints Enforce positivity and monotonicity over an entire one-dimensional domain.
- 2D Constraints Apply a global monotonicity constraint along one dimension of a noisy tensor-product fit.
- BSpline Foundations Build intuition for order, degree, knot placement, local support, and basis construction in one dimension.
- TensorSpline Foundations Understand tensor-product spline coefficients, basis matrices, and mixed partial derivatives.