  We present a novel algorithm, an adaptive-lag smoother, approximating
efficiently, in an online fashion, sequences of expectations under the marginal
smoothing distributions in general state-space models. The algorithm evolves
recursively a bank of estimators, one for each marginal, in resemblance with
the so-called particle-based, rapid incremental smoother (PaRIS). Each
estimator is propagated until a stopping criterion, measuring the fluctuations
of the estimates, is met. The presented algorithm is furnished with theoretical
results describing its asymptotic limit and memory usage.
