In this paper, a new approach for nonparametric estimation of probability density function is presented. It is based on Kolmogorov-Arnold Network that uses the activation functions on edges instead of on nodes. The experimental results show that the proposed method can obtain accuracy as high as the state-of-the-art methods while it has a compact architecture.