  Adaptive Monte Carlo schemes developed over the last years usually seek to
ensure ergodicity of the sampling process in line with MCMC tradition. This
poses constraints on what is possible in terms of adaptation. In the general
case ergodicity can only be guaranteed if adaptation is diminished at a certain
rate. Importance Sampling approaches offer a way to circumvent this limitation
and design sampling algorithms that keep adapting. Here I present a gradient
informed variant of SMC (and its special case Population Monte Carlo) for
static problems.
