Efficient maximum likelihood parameterization of continuous-time Markov processes
Efficient maximum likelihood parameterization of continuous-time Markov processes
Continuous-time Markov processes over finite state-spaces are widely used to model dynamical processes in many fields of natural and social science. Here, we introduce a maximum likelihood estimator for constructing such models from data observed at a finite time interval. This estimator is dramatically more efficient than prior approaches, enables …