  A classical problem in random number generation is the sampling of elements
from a given discrete distribution. Formally, given a set of indices $S = \{1,
\dots, n\}$ and sequence of weights $w_1, \dots, w_n \in \mathbb{R}^+$, the
task is to provide samples from $S$ with distribution $p(i) = w_i / W$ where $W
= \sum_j w_j$. A commonly accepted solution is Walker's Alias Table, which
allows for each sample to be drawn in constant time. However, some applications
correspond to a dynamic setting, where elements are inserted or removed, or
weights change over time. Here, the Alias Table is not efficient, as it needs
to be re-built whenever the underlying distribution changes.
  In this paper, we engineer a simple data structure for maintaining discrete
probability distributions in the dynamic setting. Construction of the data
structure is possible in time $O(n)$, sampling is possible in expected time
$O(1)$, and an update of size $\Delta$ can be processed in time $O(\Delta n /
W)$. As a special case, we maintain an urn containing $W$ marbles of $n$ colors
where with each update $O(W / n)$ marbles can be added or removed in $O(1)$
time per update.
  To evaluate the efficiency of the data structure in practice we conduct an
empirical study. The results suggest that the dynamic sampling performance is
competitive with the static Alias Table. Compared to existing more complex
dynamic solutions we obtain a sampling speed-up of up to half an order of
magnitude.
