Before revision:  Here, we construct a likelihood function based on the statistics of the position increments. The corresponding maximum likelihoodestimator for the global diffusion coefficient of multiple trajectories can be efficiently evaluated in \mathcal{O extend the model to mixtures of subpopulations differing in their diffusion coefficients, which we estimate by using the expectation-maximization algorithm.
After revision:  Here, we revisit the problem of extracting diffusion coefficients from single-particle tracking experiments subject to static and dynamic noise using the principle of maximum likelihood. Taking advantage of an efficient real-space formulation, we extend the model to mixtures of subpopulations differing in their diffusion coefficients, which we estimate by using the expectation-maximization algorithm.