We propose a Monte Carlo algorithm to promote Kennedy and Kuti’s linear accept/reject algorithm which accommodates unbiased stochastic estimates of the probability to an exact one. The probability upper bound violations are avoided by adopting the Metropolis accept/reject steps for both the dynamical and noise configurations and the lower bound violations can be absorbed into the observables. W...