Before revision:  As an application we study L_2-norm sampling discretization and recovery of functions in RKHS on D \subset \mathbb{R \R ^d based on random function samples , where we only assume the finite trace of the kernel . We provide several concrete estimates with precise constants for the corresponding worst-case errors.
After revision:  As an application we study L_2-norm sampling discretization and recovery of functions in RKHS on D \subset \mathbb{R \R ^d based on random function samples , where we only assume the finite trace of the kernel (Hilbert-Schmidt embedding into L_2) and provide several concrete estimates with precise constants for the corresponding worst-case errors.