Particle methods for maximum likelihood estimation in latent variable models

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. Key words: Latent Variable Models, Markov Chain Monte Carlo, Maximum Likelihood, Sequential Monte Carlo, Simulated Annealing.

• Type: Journal
• Year: 2008
• Where: SAC
• Authors: Adam M. Johansen, Arnaud Doucet, Manuel Davy
• Keywords: Applied Computing, Latent Variable Models, Monte Carlo, SAC 2008, Sequential Monte Carlo