  We propose a hierarchical tensor-network approach for approximating
high-dimensional probability density via empirical distribution. This leverages
randomized singular value decomposition (SVD) techniques and involves solving
linear equations for tensor cores in this tensor network. The complexity of the
resulting algorithm scales linearly in the dimension of the high-dimensional
density. An analysis of estimation error demonstrates the effectiveness of this
method through several numerical experiments.
