In this paper we propose a Maximum a Posteriori (MAP) approach for estimating a random sparse parameter vector in the presence of nonlinearities of unknown parameters. In this Bayesian approach, the a priori probability distribution for the parameter vector is utilised as a mechanism to promote sparsity. We solve this identification problem by using a generalized Expectation Maximization algorithm in a MAP framework.