  We consider active maximum a posteriori (MAP) inference problem for Hidden
Markov Models (HMM), where, given an initial MAP estimate of the hidden
sequence, we select to label certain states in the sequence to improve the
estimation accuracy of the remaining states. We develop an analytical approach
to this problem for the case of binary symmetric HMMs, and obtain a closed form
solution that relates the expected error reduction to model parameters under
the specified active inference scheme. We then use this solution to determine
most optimal active inference scheme in terms of error reduction, and examine
the relation of those schemes to heuristic principles of uncertainty reduction
and solution unicity.
