Bayesian Mixtures of Gaussians Notation I am having trouble understanding the following probability notations: (sorry about the page break)
$\bullet$ Consider the Bayesian mixture of Gaussians,


*

*Draw $\mu _ { k } \sim \mathcal { N } \left( 0 , \tau ^ { 2 } \right)$ for $k = 1 \ldots K$ .

*For $i = 1 \ldots n :$
(a) Draw $z _ { i } \sim \operatorname { Mult } ( \pi )$
(b) Draw $x _ { i } \sim \mathcal { N } \left( \mu _ { z _ { i } } , \sigma ^ { 2 } \right)$
Can someone please tell me what does "draw $z_i \sim Multi(π)$" mean?
This is my closest guess:
Choose k $\mu$s, from distribution $\mathcal N(0,τ^2)$.
Then do the following $n$ times:
- Randomly select a $\mu$ from the $\mu$s picked before, and randomly select an $x$ from the distribution $\mathcal N(u,σ^2)$. 
By the way, this is from a tutorial on variational inference. See bottom of page 1.
Thanks!
 A: Think about it like the following: 
$z_i \sim Mult(\pi)$ where $\pi$ is a $\textit{vector}$ of probabilities of size $K$. The multinomial distribution is a generalization of the categorical distribution, which the poster below correctly mentions. The categorical distribution is equivalent to considering the multinomial when we have one trial and more than 2 categories for consideration. In other words, the author of the document could have replace the multinomial specification with the categorical and would have been more easily interpreted. 
As a very concrete example, let's consider the case where you have just completed step (1) above and have drawn all the means, $\mu_k$ for $k = 1, \ldots, K$. Now imagine we have a $K$-sided die that you can roll, where each side has probability $\pi_k$ of being chosen for $k = 1, \ldots, K$. (Normally each side of a dice roll is equally likely but here they may not be equal). These $\pi_k$ can be collected into a vector $\pi = (\pi_{1}, \ldots, \pi_K)$, which is what we put into the "Mult" specification in part (a) above. Then, the "action":
$$
z _ { i } \sim \operatorname { Mult } ( \pi )
$$
is the same as rolling the $K$-sided die and setting $z_i$ equal to the result of of the dice roll. Here $z_i$ can take values in the range of $\{1, \ldots, K\}$. Then, $z_i$ will serve as the index of which of the means $\mu_k$ you had generated earlier will be used to now serve as the mean of the sampling distribution of $x_i$. Now repeat this process for each $i \in \{1, \ldots, n\}$. 
Finally, let's consider a real-life example. Suppose $K=3$ and $n=2$, then we will first in step (1) draw out three means $\mu_1, \mu_2, \mu_3$. Then, we have a 3-sided die for which we may roll a $1$ with probability $\pi_1$, a $2$ with probability $\pi_2$, and a $3$ with probability $\pi_3$. Then, $\pi = (\pi_1, \pi_2, \pi_3)$. Then we want to do parts (a) and (b) twice since we set $n=2$.
Roll the 3-sided die and record what we got. Let's say for the $n=1$ case we rolled a $2$. Then, we set $z_1 = 2$. For the $n=2$ case let's say we rolled a $3$. Then we set $z_2 = 3$. Now, we have:
Take the $z_1$ and $z_2$ values to serve as the index of which of the three means we want to use for sampling $x_1$ and $x_2$. This entails sampling:
$$
x _ { 1 } \sim \mathcal { N } \left( \mu _ { 2 } , \sigma ^ { 2 } \right) 
$$
and 
$$
x _ { 2 } \sim \mathcal { N } \left( \mu _ { 3 } , \sigma ^ { 2 } \right) 
$$
and plugging in the generated $\mu_2$ and $\mu_3$ from part (1) above, since $\mu_{z_1} = \mu_{2}$ and $\mu_{z_2} = \mu_{3}$.
