Parameter estimation for models with intrinsic stochasticity poses specific challenges that do not exist for deterministic models. Therefore, specialized numerical methods for parameter estimation in stochastic models have been developed. Here, we study whether dedicated algorithms for stochastic models are indeed superior to the naive approach of applying the readily available least squares algorithm designed for deterministic models. We compare the performance of the recently developed multiple shooting for stochastic systems (MSS) method designed for parameter estimation in stochastic models, a stochastic differential equations based Bayesian approach and a chemical master equation based techniques with the least squares approach for parameter estimation in models of ordinary differential equations (ODE). As test data, 1000 realizations of the stochastic models are simulated. For each realization an estimation is performed with each method, resulting in 1000 estimates for each approach. These are compared with respect to their deviation to the true parameter and, for the genetic toggle switch, also their ability to reproduce the symmetry of the switching behavior. Results are shown for different set of parameter values of a genetic toggle switch leading to symmetric and asymmetric switching behavior as well as an immigration-death and a susceptible-infected-recovered model. This comparison shows that it is important to choose a parameter estimation technique that can treat intrinsic stochasticity and that the specific choice of this algorithm shows only minor performance differences. original data (red), stochastic simulations with the MSS estimates (blue) and stochastic simulations with the LSQ estimates (dashed blue). Parameter name (column 1), true parameter value (column 2), averages of MSS estimates (column 3): Mean = 1 N ∑ i = 1 N θ ˆ i MSS , average relative errors of MSS method (column 4): ARE = 1 N ∑ i = 1 N | θ ˆ i MSS − θ 0 | θ 0 , average of the LSQ estimates (column 5): Mean = 1 N ∑ i = 1 N θ ˆ i LSQ , average relative errors of the LSQ method (column 6): ARE = 1 N ∑ i = 1 N | θ ˆ i LSQ − θ 0 | θ 0 , average of the SDE estimates (column 7): Mean = 1 N ∑ i = 1 N θ ˆ i SDE , average relative errors of the SDE method (column 8): ARE = 1 N ∑ i = 1 N | θ ˆ i SDE − θ 0 | θ 0 . 
Comparison of approaches for parameter estimation on stochastic models: Generic least squares versus specialized approaches