# What do vertical bars mean in statistical distributions?

What do the vertical bars mean in the first and third formulae?

$$v_i|z_i=k,\mu_k\sim\mathcal{N}(\mu_k, \sigma^2)$$ $$P(z_i=k)=\pi_k$$ $$\pi|\alpha\sim \text{Dir}(\alpha/K1_K)$$ $$\mu_k\sim H(\lambda)$$ This formula is originally from here.

• I think in this context the vertical bars can read as "given that". So the first line would mean $v_{i}$ given that $z_{i} = k,\mu_{k}\sim N(\mu_{k},\sigma^{2})$. See also this list on Wikipedia. – COOLSerdash Jul 31 '14 at 20:28

For example the third line can be read "pi, conditional on alpha, is distributed as dirichlet... ". The idea of a distribution conditional on something else taking a specific value is very, very common in statistics. Perhaps the most typical example would be of $Y$ values conditional on $X$ being normally distributed in regression models (for an example, see my answer here: What is the intuition behind conditional Gaussian distributions).
• "The probability of $v_i$, given that $z_i = k$ and given the value $\mu_k$, is distributed normally with mean $\mu_k$ and variance $\sigma^2$." – Sycorax Jul 31 '14 at 20:48
• It would clarify matters to define what you mean by a "conditional distribution" and explicitly distinguish it from a "conditional probability." The uses of "$|$" in the first and third lines are really quite different, despite the similarity of notation, so relying on the very same word "conditional" to distinguish them might be more confusing that enlightening. – whuber Jul 31 '14 at 21:04
• Thanks. For the particular problem in Dirichlet Process, in the first formula $$v_i|z_i=k,\mu_k\sim\mathcal{N}(\mu_k, \sigma^2)$$ it would be safe to ignore the $\mu_k$: $$v_i|z_i=k\sim\mathcal{N}(\mu_k, \sigma^2)$$ because as long as $z_{i}$belongs to the k-th cluster, its mean should be $\mu_k$. Is it necessary to put the $\mu_k$ here? Or there are some other reasons for this? – Jiang Xiang Jul 31 '14 at 21:07