  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?
