  We use a Bayesian approach to optimally solve problems in noisy binary
search. We deal with two variants:
  1. Each comparison can be erroneous with some probability $1 - p$. 2. At each
stage $k$ comparisons can be performed in parallel and a noisy answer is
returned
  We present a (classic) algorithm which optimally solves both variants
together, up to an additive term of O(\log \log(n)), and prove matching
information theoretic lower bounds. We use the algorithm to improve the results
of Farhi et al \cite{FGGS99} presenting a quantum (error free) search algorithm
in an ordered list of expected complexity less than (\log_2n) / 3.
