there are several reasons why we could be interested in sampling conditioned diffusions:

- if we observed a diffusion at discrete time and want to do some kind of inference on the parameters of the diffusion, we often need to interpolate in between (missing data)
- physicists/chemist often study the behaviour of a particle in a double well potential: how do you simulate one transition between the two meta-stable positions ?

To be concrete, let us say that we want to sample/study (compute some reasonable approximation of path functionals, say) the conditioned SDE $$dX_t = -\nabla U(X_t) \ dt + dW_t, \qquad X_0=x_{-}, \; X_{T}=x^+.$$ If $T$ is sufficiently small, since this is easy to write the density of the conditioned SDE with respect to the Brownian bridge measure, under mild assumptions on the potential $U$ we could hope that an accept-reject approach might work: this is not the case we are interested. Instead, we suppose that $T$ is large in some sense so that a Brownian bridge is very unlikely to look like a path of this conditioned SDE: the accept reject method does not work.

**Question**: what is the right way to attack this problem ?

- we could start from any path, and at each iteration choose random times $t$ and $t+\Delta t$ and update in between by the usual accept-reject method: if $\Delta t$ is small enough, this might be not too bad.
- we could run a MCMC on the (discretized) path space - articles have been written, this works reasonably well in theory, does not work in practice
- we could try a h-transform: we do not know or want to approximate the transition densities of the conditioned diffusion

Since this situation looks sufficiently general, and should have showed up in different places, I was wondering if there were fundamentally different (i.e. better) approaches to tackle the curse of dimensionality (ie: $T$ is large) inherent to this problem.