  Motivated by the need of the linking records across various databases, we
propose a novel graphical model based classifier that uses a mixture of Poisson
distributions with latent variables. The idea is to derive insight into each
pair of hypothesis records that match by inferring its underlying latent rate
of error using Bayesian Modeling techniques. The novel approach of using gamma
priors for learning the latent variables along with supervised labels is unique
and allows for active learning. The naive assumption is made deliberately as to
the independence of the fields to propose a generalized theory for this class
of problems and not to undermine the hierarchical dependencies that could be
present in different scenarios. This classifier is able to work with sparse and
streaming data. The application to record linkage is able to meet several
challenges of sparsity, data streams and varying nature of the data-sets.
