= Posterior structural-zero probability
{title2=$P(Z=1\mid Y=0)=\frac{\pi}{\pi+(1-\pi)e^{-\mu}}$}
In a <zero-inflated Poisson regression>, <Bayes theorem> separates structural zeros from susceptible zero counts. A positive count rules out the structural-zero class; a zero raises its posterior probability according to the displayed formula, but usually does not determine class membership with certainty. This same probability supplies the E-step of the <EM algorithm for zero-inflated Poisson regression>.
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