Probabilistic word embedding models have emerged as a way to model textual data in scientific applications [Rudolph We introduce probabilistic embeddings using et al., 2016, Bamler and Mandt, 2017]. The advantages include Laplacian priors (PELP). The proposed model enables flexible inclusion of prior knowledge, explicit handling incorporating graph side-information into of uncertainty, straightforward estimation, and usefulness static word embeddings. We theoretically show in decision-making [Ghahramani, 2015]. More versatile that the model unifies several previously proposed and flexible word embedding methods allow for increasingly embedding methods under one umbrella.