A Modulated Parzen-Windows Approach for Probability Density Estimation

The Parzen-window approach is a well-known technique for estimating probability density functions. This paper introduces a modulated Parzen-windows approach. This approach uses kernels at equidistant samples to obtain a probability density function more efficiently. Experiments on both artificial and real data show that the modulated Parzen-windows approach is more efficient in probability density function estimation, without costly preprocessing or severe loss of accuracy.