  An understanding of how input parameter uncertainty in the numerical
simulation of physical models leads to simulation output uncertainty is a
challenging task. Common methods for quantifying output uncertainty, such as
performing a grid or random search over the model input space, are
computationally intractable for a large number of input parameters, represented
by a high-dimensional input space. It is therefore generally unclear as to
whether a numerical simulation can reproduce a particular outcome (e.g. a set
of experimental results) with a plausible set of model input parameters. Here,
we present a method for efficiently searching the input space using Bayesian
Optimisation to minimise the difference between the simulation output and a set
of experimental results. Our method allows explicit evaluation of the
probability that the simulation can reproduce the measured experimental results
in the region of input space defined by the uncertainty in each input
parameter. We apply this method to the simulation of charge-carrier dynamics in
the perovskite semiconductor methyl-ammonium lead iodide MAPbI$_3$ that has
attracted attention as a light harvesting material in solar cells. From our
analysis we conclude that the formation of large polarons, quasiparticles
created by the coupling of excess electrons or holes with ionic vibrations,
cannot explain the experimentally observed temperature dependence of electron
mobility.
