  Sequential importance sampling algorithms have been defined to estimate
likelihoods in models of ancestral population processes. However, these
algorithms are based on features of the models with constant population size,
and become inefficient when the population size varies in time, making
likelihood-based inferences difficult in many demographic situations. In this
work, we modify a previous sequential importance sampling algorithm to improve
the efficiency of the likelihood estimation. Our procedure is still based on
features of the model with constant size, but uses a resampling technique with
a new resampling probability distribution depending on the pairwise composite
likelihood. We tested our algorithm, called sequential importance sampling with
resampling (SISR) on simulated data sets under different demographic cases. In
most cases, we divided the computational cost by two for the same accuracy of
inference, in some cases even by one hundred. This study provides the first
assessment of the impact of such resampling techniques on parameter inference
using sequential importance sampling, and extends the range of situations where
likelihood inferences can be easily performed.
