Sampling scattered data with Bernstein polynomials: stochastic and deterministic error estimates
Viewing the classical Bernstein polynomials as sampling operators, we study a generalization by allowing the sampling operation to take place at scattered sites. We utilize both stochastic and deterministic approaches. On the stochastic side, we consider the sampling sites as random variables that obey some naturally derived probabilistic distributions, and obtain Chebyshev type estimates. On the deterministic side, we incorporate the theory of uniform distribution of point sets (within the framework of Weyl's criterion) and the discrepancy method. We establish convergence results and error estimates under practical assumptions on the distribution of the sampling sites.
Wu, Zongmin, Xingping Sun, and Limin Ma. "Sampling scattered data with Bernstein polynomials: stochastic and deterministic error estimates." Advances in Computational Mathematics 38, no. 1 (2013): 187-205.
Advances in Computational Mathematics