1. A method comprising: receiving a plurality of sets of time-sequential signal observations for each of a number K of signal repetitions, wherein each set of signal observations is associated with a respective dimension of a K-dimensional time grid having time-indexed points; at each of a plurality of the time-indexed points, calculating with a processor a state cost metric for each state in a set of states of a hidden Markov model (HMM), wherein for each state in the set of states and for a given time-indexed point, the state cost metric calculation provides a most-likely predecessor state and a corresponding most-likely predecessor time-indexed point; determining a sequence of states using the calculated state cost metrics and determining a corresponding cumulative probability measure for the HMM.