A data-cloning SMC2 algorithm is proposed as a general-purpose, global optimization routine for the maximum likelihood estimation of models with latent variables. In the SMC2 phase, the method first marginalizes out the latent variable(s) by applying one layer of SMC at a fixed parameter value and then searches for the optimal parameters through another layer of SMC. The data-cloning phase is deployed to ensure global convergence by dampening multi-modality and to reduce the Monte Carlo error associated with SMC. This new method has broad applicability and is massively parallelizable through leveraging modern multi-core CPU or GPU computing.
