Abstract
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 |
|---|---|
| Original language | English |
| Pages (from-to) | 47 - 57 |
| Number of pages | 11 |
| Journal | Statistics and Computing |
| Volume | 18 (1) |
| DOIs | |
| Publication status | Published - Mar 2008 |