Histogram Density Estimation is one of the primitive and easiest non-parametric density estimation methods. The given feature space is divided into equally-spaced bins or cells. The number of training feature samples that fall into each category is computed, (i.e) if '$ n_i $' is the number of feature samples that fall into the bin 'i', and 'V' is the volume of each cell (the volume of all the cells are equal because they are equi-spaced), the density is given by $ p(x) = n_i/V $.
