  We study the Thompson sampling algorithm in an adversarial setting,
specifically, for adversarial bit prediction. We characterize the bit sequences
with the smallest and largest expected regret. Among sequences of length $T$
with $k < \frac{T}{2}$ zeros, the sequences of largest regret consist of
alternating zeros and ones followed by the remaining ones, and the sequence of
smallest regret consists of ones followed by zeros. We also bound the regret of
those sequences, the worse case sequences have regret $O(\sqrt{T})$ and the
best case sequence have regret $O(1)$.
  We extend our results to a model where false positive and false negative
errors have different weights. We characterize the sequences with largest
expected regret in this generalized setting, and derive their regret bounds. We
also show that there are sequences with $O(1)$ regret.
