  The past decades have seen enormous improvements in computational inference
based on statistical models, with continual enhancement in a wide range of
computational tools, in competition. In Bayesian inference, first and foremost,
MCMC techniques continue to evolve, moving from random walk proposals to
Langevin drift, to Hamiltonian Monte Carlo, and so on, with both theoretical
and algorithmic inputs opening wider access to practitioners. However, this
impressive evolution in capacity is confronted by an even steeper increase in
the complexity of the models and datasets to be addressed. The difficulties of
modelling and then handling ever more complex datasets most likely call for a
new type of tool for computational inference that dramatically reduce the
dimension and size of the raw data while capturing its essential aspects.
Approximate models and algorithms may thus be at the core of the next
computational revolution.
