1. A method comprising: receiving information identifying and describing at least one of a set of events, an initial distribution of a plurality of states, an initial transition matrix, or an initial event matrix; generating, based at least in part on the information, at least one intermediate transition matrix and at least one intermediate event matrix describing a sparse Baum-Welch training that allows no event to occur at one or more time steps; and transforming, using one or more processors, the at least one intermediate transition matrix and the at least one intermediate event matrix into a transition matrix and an event matrix describing a continuous-time Baum-Welch training, the continuous-time Baum-Welch training allowing events to occur simultaneously or at sporadic time intervals in a Markov model including a Hidden Markov Model (HMM) having more than two hidden states.