Plugging O(N) smoothing algos in PGibbs

[AdrienCorenflos]

This makes the new O(N) smoothing algos available for backward sampling in CSMC.

This is not very useful for cheap models, as the cost of sampling a single trajectory is still O(N) (because we need to sample from a categorical of the filtering weights), not O(1) but useful for models with expensive transition dynamics, in which case the cost is dominated by evaluating many possible trajectories.

I'm currently working with such a model in a nested particle filter context, where the cost of evaluating a transition is O(M) where M is the number of inner particles to the PF. This has made a massive difference.