This work explores CS-RBFs as novel probability density functions.
problem Underexplored use of CS-RBFs in statistics and probability modeling.
method Derivation of statistical properties of CS-RBFs as univariate and conditional densities, analysis of mixture models, and introduction of an incremental learning algorithm.
result CS-RBF densities provide competitive results in likelihood and model complexity compared to Gaussian mixture models.