This paper focuses on Optimization Methods based on Probability Models (OMPM) that statistically estimate the distribution of promising solutions from obtained samples and draw new samples from the estimated distribution, and proposes a novel method for OMPM to improve the accuracy of the statistical estimations by maintaining the previously generated samples more precisely than the conventional methods like Estimation of Distribution Algorithms. The key idea of the proposed method is to update the population (i.e., the set of samples) to follow the target distribution by weighting generated samples and resampling them according to importance sampling. Experimental comparisons between the proposed method and a conventional method have revealed the following two advantages of the proposed method: (1) the proposed method finds better solutions than the conventional method; and (2) the proposed method can control the convergence speed.