  Sequential Monte Carlo (SMC) methods are a class of techniques to sample
approximately from any sequence of probability distributions using a
combination of importance sampling and resampling steps. This paper is
concerned with the convergence analysis of a class of SMC methods where the
times at which resampling occurs are computed online using criteria such as the
effective sample size. This is a popular approach amongst practitioners but
there are very few convergence results available for these methods. By
combining semigroup techniques with an original coupling argument, we obtain
functional central limit theorems and uniform exponential concentration
estimates for these algorithms.
