This is an old version, view current version.

## 27.3 Sampling from the posterior predictive distribution

Given draws from the posterior $$\theta^{(m)} \sim p(\theta \mid y),$$ draws from the posterior predictive $$\tilde{y}^{(m)} \sim p(\tilde{y} \mid y)$$ can be generated by randomly generating from the sampling distribution with the parameter draw plugged in, $\tilde{y}^{(m)} \sim p(y \mid \theta^{(m)}).$

Randomly drawing $$\tilde{y}$$ from the sampling distribution is critical because there are two forms of uncertainty in posterior predictive quantities, sampling uncertainty and estimation uncertainty. Estimation uncertainty arises because $$\theta$$ is being estimated based only on a sample of data $$y$$. Sampling uncertainty arises because even a known value of $$\theta$$ leads to a sampling distribution $$p(\tilde{y} \mid \theta)$$ with variation in $$\tilde{y}$$. Both forms of uncertainty show up in the factored form of the posterior predictive distribution, $p(\tilde{y} \mid y) = \int \underbrace{p(\tilde{y} \mid \theta)}_{\begin{array}{l} \textrm{sampling} \\[-2pt] \textrm{uncertainty} \end{array}} \cdot \underbrace{p(\theta \mid y)}_{\begin{array}{l} \textrm{estimation} \\[-2pt] \textrm{uncertainty} \end{array}} \, \textrm{d}\theta.$