A Bayesian analysis of zero-inflated generalized Poisson model

In several real-life examples one encounters count data where the number of zeros is such that the usual Poisson distribution does not fit the data. Quite often the number of zeros is large, and hence the data is zero inflated. In this situation, a zero-inflated generalized Poisson model can be considered and a Bayesian analysis can be carried out. Some appropriate priors are discussed and the posteriors are obtained using Monte-Carlo integration with importance sampling. The predictive density of the future observation is also obtained. The techniques are illustrated using a real-life data set. Computations largely support the methodology.

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