Many filtering applications have continuous state dynamics X-t = integral(0)(t) m(X-S) ds + sigma W-t + rho, discrete observations Y-j = Y-tj, and nonadditive or non-Gaussian observation noise. One wants to calculate the conditional probability Pr {X-t is an element of d(z) \ Y-3, 0 less than or equal to t(j) less than or equal to t} economically. Herein, we show that a combination of convolution, scaling, and substitutions efficiently solves this problem under certain conditions. Our method is easy to use and assumes nothing about the observations other than the ability to construct p(Yj \ Xtj), the conditional density of the jth observation given the current state.