In this paper, we considered a Bayesian hierarchical method using the hyper product inverse moment
prior in the ultrahigh-dimensional generalized linear model (UDGLM), that was useful in the Bayesian
variable selection. We showed the posterior probabilities of the true model converge to 1 as the sample
size increases. For computing the posterior probabilities, we implemented the Laplace approximation.
The Simplified Shotgun Stochastic Search with Screening (S5) procedure for generalized linear model
was suggested for exploring the posterior space. Simulation studies and real data analysis using the
Bayesian ultrahigh-dimensional generalized linear model indicate that the proposed method had better performance than the previous models.