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I am reading article about bayesian predictive function. In the article it denote posterior distribution $\pi_n(d\theta) = \frac {\prod^n_{i=1}f(y_i|\theta) \pi(d\theta)}{\int \prod^n_{i=1}f(y_i|\theta)\pi(d\theta')}$ where $\pi$ is prior for $\theta$ and $\pi_n$ is posterior given $(y_1, y_2, ... ,y_n)$. In the addition, $Y_1, Y_2, ..., Y_n$ are iid. The article also denote predictive density like that $p_n(y) = \int f(y|\theta)\pi_n(d\theta)$. But I don't know what the $d\theta$ means in this case.

Thanks for everyone who will help me.

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