  Grain surface chemistry and its treatment in gas-grain chemical models is an
area of large uncertainty. Whilst laboratory experiments are making progress,
there is still much that is unknown about grain surface chemistry. Further, the
results and parameters produced by experiment are often not easily translated
to the rate equation approach most commonly used in astrochemical modelling. It
is possible that statistical methods can reduce the uncertainty in grain
surface chemical networks. In this work, a simple model of grain surface
chemistry in a molecular cloud is developed and a Bayesian inference of the
reactions rates is performed through MCMC sampling. Using observational data of
the solid state abundances of major chemical species in molecular clouds, the
posterior distributions for the rates of seven reactions producing CO, CO$_2$,
CH$_3$OH and H$_2$O are calculated, in a form that is suitable for rate
equation models. This represents a vital first step in the development of a
method to infer reaction rates from observations of chemical abundances in
astrophysical environments.
