The use of a Wiener filter estimate for the linear transfer function can significantly improve the description of behavioral dynamics. This report presents a two-pass, Monte-Carlo-based algorithm that is well suited to repeated-trials local average measurements. The Wiener filter transfer functions strongly suppress noise artifacts as well as allow reliable transfer function determination under a much wider class of reinforcement schedules. Implications of expanding the possible form of experimental design are considered along with improvements in the fidelity of resulting predictions. (Contains 5 figures and 1 footnote.)