Comment on "Sequential Monte Carlo for Bayesian Computation" (P. Del   Moral, A. Doucet, A. Jasra)
  The main question concerns another recent advance in sequential Monte Carlo, the use of a mixture transition kernel that automatically adapts to the target distribution (Douc et al. 2006). Is there a class of static inference problems for which the backward-kernel approach is better suited, or is it too early to predict which method may have better performance in a particular situation? 
David R. Bickel