  Ranked voting systems, such as instant-runoff voting (IRV) and single
transferable vote (STV), are used in many places around the world. They are
more complex than plurality and scoring rules, presenting 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 statistical
model, a Dirichlet-tree, that can cope with high-dimensional parameters in a
computationally efficient manner. We demonstrate this approach 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.
