Non-Markovian optimal prediction

Optimal prediction methods compensate for a lack of resolution in the numerical so lution of complex problems through the use of prior statistical Information. We know from previous work that in the presence of strong underresolution a good approximation needs a non-Markovian memory, determined by an equation for the orthogonal, i.e., unresolved, dynamics. We present a simple approximation of the orthogonal dynamics, which involves an ansatz and a Monte-Carlo evaluation of autocorrelations. The analysis provides a new understanding of the fluctuation-dissipation formulas of statistical physics. An example is given.