  This paper proposes a new estimation technique for fitting parametric Gibbs
point process models to a spatial point pattern dataset. The technique is a
counterpart, for spatial point processes, of the variational estimators for
Markov random fields developed by Almeida and Gidas. The estimator does not
require the point process density to be hereditary, so it is applicable to
models which do not have a conditional intensity, including models which
exhibit geometric regularity or rigidity. The disadvantage is that the
intensity parameter cannot be estimated: inference is effectively conditional
on the observed number of points. The new procedure is faster and more stable
than existing techniques, since it does not require simulation, numerical
integration or optimization with respect to the parameters.
