  When classical particle filtering algorithms are used for maximum likelihood
parameter estimation in nonlinear state-space models, a key challenge is that
estimates of the likelihood function and its derivatives are inherently noisy.
The key idea in this paper is to run a particle filter based on a current
parameter estimate, but then use the output from this particle filter to
re-evaluate the likelihood function approximation also for other parameter
values. This results in a (local) deterministic approximation of the likelihood
and any standard optimization routine can be applied to find the maximum of
this local approximation. By iterating this procedure we eventually arrive at a
final parameter estimate.
