The accuracy of simulation of sub-Gaussian random fields in some functional spaces (Q2739583)

A method for approximation of sub-Gaussian random fields that can be represented as a stochastic integral NEWLINE\[NEWLINEX(\vec{t})=\sum_{r=1}^N\int_{R^d}f_r(\vec{t},\vec{\lambda}) dZ_r(\vec{\lambda})NEWLINE\]NEWLINE by the random fields NEWLINE\[NEWLINEX_n(\vec{t},A)=\sum_{r=1}^N\sum_{i=1}^nf_r(\vec{t},\vec{\lambda}_i) Z_r(\Delta_i),NEWLINE\]NEWLINE where \(Z_r(\Delta_i)\) are stochastic measures, \(A=\bigcup_{i=1}^nA_i, \vec{\lambda}_i\in \Delta_i,\) is proposed. The model gives approximation for such random fields with a given reliability and accuracy in \(L_p(T),T\subset R^d,\) and Orlicz spaces.