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.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.