  We have utilized the non-conjugate VB method for the problem of the sparse
Poisson regression model. To provide an approximated conjugacy in the model,
the likelihood is approximated by a quadratic function, which provides the
conjugacy of the approximation component with the Gaussian prior to the
regression coefficient. Three sparsity-enforcing priors are used for this
problem. The proposed models are compared with each other and two frequentist
sparse Poisson methods (LASSO and SCAD) to evaluate the prediction performance,
as well as, the sparsing performance of the proposed methods. Throughout a
simulated data example, the accuracy of the VB methods is computed compared to
the corresponding benchmark MCMC methods. It can be observed that the proposed
VB methods have provided a good approximation to the posterior distribution of
the parameters, while the VB methods are much faster than the MCMC ones. Using
several benchmark count response data sets, the prediction performance of the
proposed methods is evaluated in real-world applications.
