Weighted parametric divergence models for discrete probability distributions

In the literature of information measures, it is well acknowledged phenomenon that distance models in probability spaces discover incredible applications in a diversity of disciplines related with science and technology. The significance of these models after attaching weights to the occurring events cannot be disregarded. The present communication is a footstep in the construction of such divergence models. We have developed two new weighted parametric divergence models for the discrete probability distributions and proved their legitimacy after studying their indispensable properties.