If you had Monte Carlo samples from the posterior distribution of possible transition matrices, then you could calculate powers of each sample matrix and get a distribution of long-term behaviors. There are algorithms to compute such posteriors; perhaps this could be made more efficient using a sequential Monte Carlo method to update the posterior after each measured state. But I imagine that still would be an extremely inefficient way to do this, not really exploiting the sparsity of the measurements.