  This paper considers the problem of determining an optimal control action
based on observed data. We formulate the problem assuming that the system can
be modelled by a nonlinear state-space model, but where the model parameters,
state and future disturbances are not known and are treated as random
variables. Central to our formulation is that the joint distribution of these
unknown objects is conditioned on the observed data. Crucially, as new
measurements become available, this joint distribution continues to evolve so
that control decisions are made accounting for uncertainty as evidenced in the
data. The resulting problem is intractable which we obviate by providing
approximations that result in finite dimensional deterministic optimisation
problems. The proposed approach is demonstrated in simulation on a nonlinear
system.
