In [3]:

```
%matplotlib inline
```

**Note**: This is best viewed on NBViewer. It is part of a series on Dirichlet Processes and Nonparametric Bayes.

The symmetric Dirichlet distribution (DD) can be considered a distribution of distributions. Each sample from the DD is a categorial distribution over $K$ categories. It is parameterized $G_0$, a distribution over $K$ categories and $\alpha$, a scale factor.

The expected value of the DD is $G_0$. The variance of the DD is a function of the scale factor. When $\alpha$ is large, samples from $DD(\alpha\cdot G_0)$ will be very close to $G_0$. When $\alpha$ is small, samples will vary more widely.

We demonstrate below by setting $G_0=[.2, .2, .6]$ and varying $\alpha$ from 0.1 to 1000. In each case, the mean of the samples is roughly $G_0$, but the standard deviation is decreases as $\alpha$ increases.

In [10]:

```
import numpy as np
from scipy.stats import dirichlet
np.set_printoptions(precision=2)
def stats(scale_factor, G0=[.2, .2, .6], N=10000):
samples = dirichlet(alpha = scale_factor * np.array(G0)).rvs(N)
print " alpha:", scale_factor
print " element-wise mean:", samples.mean(axis=0)
print "element-wise standard deviation:", samples.std(axis=0)
print
for scale in [0.1, 1, 10, 100, 1000]:
stats(scale)
```

The Dirichlet Process can be considered a way to *generalize* the Dirichlet distribution. While the Dirichlet distribution is parameterized by a discrete distribution $G_0$ and generates samples that are similar discrete distributions, the Dirichlet process is parameterized by a generic distribution $H_0$ and generates samples which are distributions similar to $H_0$. The Dirichlet process also has a parameter $\alpha$ that determines how similar how widely samples will vary from $H_0$.

We can construct a sample $H$ (recall that $H$ is a probability distribution) from a Dirichlet process $\text{DP}(\alpha H_0)$ by drawing a countably infinite number of samples $\theta_k$ from $H_0$) and setting:

$$H=\sum_{k=1}^\infty \pi_k \cdot\delta(x-\theta_k)$$where the $\pi_k$ are carefully chosen weights (more later) that sum to 1. ($\delta$ is the Dirac delta function.)

$H$, a sample from $DP(\alpha H_0)$, is a *probability distribution* that looks similar to $H_0$ (also a distribution). In particular, $H$ is a *discrete* distribution that takes the value $\theta_k$ with probability $\pi_k$. This sampled distribution $H$ is a discrete distribution _even if $H_0$ has continuous support_; the support of $H$ is a countably infinite subset of the support $H_0$.

The weights ($\pi_k$ values) of a Dirichlet process sample related the Dirichlet *process* back to the Dirichlet *distribution*.

Gregor Heinrich writes:

The defining property of the DP is that its samples have weights $\pi_k$ and locations $\theta_k$ distributed in such a way that when partitioning $S(H)$ into finitely many arbitrary disjoint subsets $S_1, \ldots, S_j$ $J<\infty$, the sums of the weights $\pi_k$ in each of these $J$ subsets are distributed according to a Dirichlet distribution that is parameterized by $\alpha$ and a discrete base distribution (like $G_0$) whose weights are equal to the integrals of the base distribution $H_0$ over the subsets $S_n$.

As an example, Heinrich imagines a DP with a standard normal base measure $H_0\sim \mathcal{N}(0,1)$. Let $H$ be a sample from $DP(H_0)$ and partition the real line (the support of a normal distribution) as $S_1=(-\infty, -1]$, $S_2=(-1, 1]$, and $S_3=(1, \infty]$ then

$$H(S_1),H(S_2), H(S_3) \sim \text{Dir}\left(\alpha\,\text{erf}(-1), \alpha\,(\text{erf}(1) - \text{erf}(-1)), \alpha\,(1-\text{erf}(1))\right)$$where $H(S_n)$ be the sum of the $\pi_k$ values whose $\theta_k$ lie in $S_n$.

These $S_n$ subsets are chosen for convenience, however similar results would hold for *any* choice of $S_n$. For any sample from a Dirichlet *process*, we can construct a sample from a Dirichlet *distribution* by partitioning the support of the sample into a finite number of bins.

There are several equivalent ways to choose the $\pi_k$ so that this property is satisfied: the Chinese restaurant process, the stick-breaking process, and the PĆ³lya urn scheme.

To generate $\left\{\pi_k\right\}$ according to a stick-breaking process we define $\beta_k$ to be a sample from $\text{Beta}(1,\alpha)$. $\pi_1$ is equal to $\beta_1$. Successive values are defined recursively as

$$\pi_k=\beta_k \prod_{j=1}^{k-1}(1-\beta_j).$$Thus, if we want to draw a sample from a Dirichlet process, we could, in theory, sample an infinite number of $\theta_k$ values from the base distribution $H_0$, an infinite number of $\beta_k$ values from the Beta distribution. Of course, sampling an infinite number of values is easier in theory than in practice.

However, by noting that the $\pi_k$ values are *positive* values summing to 1, we note that, in expectation, they must get increasingly small as $k\rightarrow\infty$. Thus, we can reasonably approximate a sample $H\sim DP(\alpha H_0)$ by drawing *enough* samples such that $\sum_{k=1}^K \pi_k\approx 1$.

We use this method below to draw approximate samples from several Dirichlet processes with a standard normal ($\mathcal{N}(0,1)$) base distribution but varying $\alpha$ values.

Recall that a single sample from a Dirichlet process is a probability distribution over a countably infinite subset of the support of the base measure.

The blue line is the PDF for a standard normal. The black lines represent the $\theta_k$ and $\pi_k$ values; $\theta_k$ is indicated by the position of the black line on the $x$-axis; $\pi_k$ is proportional to the height of each line.

We generate enough $\pi_k$ values are generated so their sum is greater than 0.99. When $\alpha$ is small, very few $\theta_k$'s will have corresponding $\pi_k$ values larger than $0.01$. However, as $\alpha$ grows large, the sample becomes a more accurate (though still discrete) approximation of $\mathcal{N}(0,1)$.

In [13]:

```
import matplotlib.pyplot as plt
from scipy.stats import beta, norm
def dirichlet_sample_approximation(base_measure, alpha, tol=0.01):
betas = []
pis = []
betas.append(beta(1, alpha).rvs())
pis.append(betas[0])
while sum(pis) < (1.-tol):
s = np.sum([np.log(1 - b) for b in betas])
new_beta = beta(1, alpha).rvs()
betas.append(new_beta)
pis.append(new_beta * np.exp(s))
pis = np.array(pis)
thetas = np.array([base_measure() for _ in pis])
return pis, thetas
def plot_normal_dp_approximation(alpha):
plt.figure()
plt.title("Dirichlet Process Sample with N(0,1) Base Measure")
plt.suptitle("alpha: %s" % alpha)
pis, thetas = dirichlet_sample_approximation(lambda: norm().rvs(), alpha)
pis = pis * (norm.pdf(0) / pis.max())
plt.vlines(thetas, 0, pis, )
X = np.linspace(-4,4,100)
plt.plot(X, norm.pdf(X))
plot_normal_dp_approximation(.1)
plot_normal_dp_approximation(1)
plot_normal_dp_approximation(10)
plot_normal_dp_approximation(1000)
```

Often we want to draw samples from a *distribution sampled from a Dirichlet process* instead of from the Dirichlet process itself. Much of the literature on the topic unhelpful refers to this as sampling from a Dirichlet process.

Fortunately, we don't have to draw an infinite number of samples from the base distribution and stick breaking process to do this. Instead, we can draw these samples *as they are needed*.

Suppose, for example, we know a finite number of the $\theta_k$ and $\pi_k$ values for a sample $H\sim \text{Dir}(\alpha H_0)$. For example, we know

$$\pi_1=0.5,\; \pi_3=0.3,\; \theta_1=0.1,\; \theta_2=-0.5.$$To sample from $H$, we can generate a uniform random $u$ number between 0 and 1. If $u$ is less than 0.5, our sample is $0.1$. If $0.5<=u<0.8$, our sample is $-0.5$. If $u>=0.8$, our sample (from $H$ will be a new sample $\theta_3$ from $H_0$. At the same time, we should also sample and store $\pi_3$. When we draw our next sample, we will again draw $u\sim\text{Uniform}(0,1)$ but will compare against $\pi_1, \pi_2$, AND $\pi_3$.

The class below will take a base distribution $H_0$ and $\alpha$ as arguments to its constructor. The class instance can then be called to generate samples from $H\sim \text{DP}(\alpha H_0)$.

In [20]:

```
from numpy.random import choice
class DirichletProcessSample():
def __init__(self, base_measure, alpha):
self.base_measure = base_measure
self.alpha = alpha
self.cache = []
self.weights = []
self.total_stick_used = 0.
def __call__(self):
remaining = 1.0 - self.total_stick_used
i = DirichletProcessSample.roll_die(self.weights + [remaining])
if i is not None and i < len(self.weights) :
return self.cache[i]
else:
stick_piece = beta(1, self.alpha).rvs() * remaining
self.total_stick_used += stick_piece
self.weights.append(stick_piece)
new_value = self.base_measure()
self.cache.append(new_value)
return new_value
@staticmethod
def roll_die(weights):
if weights:
return choice(range(len(weights)), p=weights)
else:
return None
```

This Dirichlet process class could be called *stochastic memoization*. This idea was first articulated in somewhat abstruse terms by Daniel Roy, et al.

Below are histograms of 10000 samples drawn from *samples* drawn from Dirichlet processes with standard normal base distribution and varying $\alpha$ values.

In [22]:

```
import pandas as pd
base_measure = lambda: norm().rvs()
n_samples = 10000
samples = {}
for alpha in [1, 10, 100, 1000]:
dirichlet_norm = DirichletProcessSample(base_measure=base_measure, alpha=alpha)
samples["Alpha: %s" % alpha] = [dirichlet_norm() for _ in range(n_samples)]
_ = pd.DataFrame(samples).hist()
```

Note that these histograms look very similar to the corresponding plots of sampled distributions above. However, these histograms are plotting *points sampled from a distribution sampled from a Dirichlet process* while the plots above were showing approximate *distributions samples from the Dirichlet process*. Of course, as the number of samples from each $H$ grows large, we would expect the histogram to be a very good empirical approximation of $H$.

In a future post, I will look at how this `DirichletProcessSample`

class can be used to draw samples from a *hierarchical* Dirichlet process.

In [ ]:

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