Maximum Likelihood Estimation of Latent Variable Models by SMC with Marginalization and Data Cloning
24 Pages Posted: 27 Sep 2017
There are 2 versions of this paper
Maximum Likelihood Estimation of Latent Variable Models by SMC with Marginalization and Data Cloning
Data-Cloning SMC 2 for Applications to Latent Variable Models
Date Written: August 24, 2017
Abstract
A data-cloning SMC² method is proposed as a general purpose optimization routine for estimating latent variable models by maximum likelihood. The latent variables are first marginalized out by SMC at any fixed parameter value, and the model parameters are then estimated by density tempered SMC. The data-cloning step is employed to efficiently reduce Monte Carlo errors inherent in the SMC² algorithm and also to effectively address multi-modality present in typical objective functions. This new method has wide applicability and can be massively parallelized to take advantage of typical computers today.
Keywords: Sequential Monte Carlo, Data Clone, Latent Variable, Maximum Likelihood, Monte Carlo Optimization
JEL Classification: C15, C63
Suggested Citation: Suggested Citation