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$\newcommand{\Dirichlet}{\operatorname{Dirichlet}}$In a Dirichlet Process, where $P \sim DP(\alpha P_0)$ and $y_i \sim P$ are iid, the posterior distribution of a Dirichlet Process $P$ is usually given as:

$$ P \mid y_1, \ldots y_n \sim DP\left(\alpha P_0 + \sum_{i}^{n}\delta_{y_i}\right) $$ where $\delta_{y_i}(x)$ is the Dirac delta mass function taking on value 1 if $x=y_i$.

It is also equivalent that for any measurable partition $B_1, \ldots, B_k$:

$$ P(B_1), \ldots, P(B_k) \mid y_1, \ldots y_n \sim \Dirichlet\left(\alpha P_0(B_1)+\sum_{i}^{n}\mathbb{1}_{y_i \in B_1}, \ldots, \alpha P_0(B_k)+\sum_{i}^{n}\mathbb{1}_{y_i \in B_k}\right) $$

In the limiting case, for $\alpha \to 0$, we have that:

$$ P(B_1), \ldots, P(B_k) \mid y_1, \ldots y_n \sim \Dirichlet\left(\sum_{i}^{n}\mathbb{1}_{y_i \in B_1}, \ldots, \sum_{i}^{n}\mathbb{1}_{y_i \in B_k}\right) $$

Here, suppose I had some data and I wanted to plot realizations from the posterior of the limiting case above. In order to do so, I must specify bin size before I can sort the data into bins.

Question: Is there any general guideline for specifying the bin size?

My understanding of the Dirichlet Process is that it sort of is independent of having to specify a bin size, so I am very confused about how to do this. In general, what I seek to do is:

1) First specify number of bins $k$.
2) Then sort the data into the bins.
3) Then each bin will have a corresponding count, i.e., 4 data points in first bin, 2 in second, etc.
4) Put the counts into the Dirichlet above to get realizations.

Do I fix $k$ here? thanks.

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    $\begingroup$ What bins are you talking about? Do you mean what partition to use? $\endgroup$ – jaradniemi Dec 6 '15 at 2:27
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In the limiting case, when $\alpha \rightarrow 0$, all samples from $P$ will have the same value. This can easily be illustrated if we look at the predictive distributions of $y_1$ and $y_2$: $$ \begin{eqnarray*} y_1 &\sim& P_0 \\ y_2|y_1 &\sim& \frac{\alpha}{\alpha+1} P_0 + \frac{1}{\alpha+1}\delta_{y_1} \end{eqnarray*} $$

Except for the first sample, $y_1$, all others will have zero probability of getting a new value drawn from the base distribution, $P_0$, and will almost surely form a single cluster.

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