Ranked voting systems, such as instant-runo voting (IRV)
and single transferable vote (STV), are used in many places around the
world. They are more complex than plurality and scoring rules, pre-
senting a challenge for auditing their outcomes: there is no known risk-
limiting audit (RLA) method for STV other than a full hand count.
We present a new approach to auditing ranked systems that uses a sta-
tistical model, a Dirichlet-tree, that can cope with high-dimensional pa-
rameters in a computationally e cient manner. We demonstrate this ap-
proach with a ballot-polling Bayesian audit for IRV elections. Although
the technique is not known to be risk-limiting, we suggest some strategies
that might allow it to be calibrated to limit risk