Standard methods for maximum likelihood parameter estimation in latent variable models rely on the Expectation-Maximization algorithm and its Monte Carlo variants. Our approach is different and motivated by similar considerations to simulated annealing; that is we build a sequence of artificial distributions whose support concentrates itself on the set of maximum likelihood estimates. We sample from these distributions using a sequential Monte Carlo approach. We demonstrate state of the art performance for several applications of the proposed approach.
|Translated title of the contribution||Particle methods for maximum likelihood parameter estimation in latent variable models|
|Pages (from-to)||47 - 57|
|Number of pages||11|
|Journal||Statistics and Computing|
|Publication status||Published - Mar 2008|