Julian Besag's contributions to the discipline of statistics are profound, and have been, and continue to be, of far-reaching consequence. His research work had authority and great originality.

The area in which he made the biggest impression is the conditional modelling of spatial systems. This helped to lay the foundations for the entire contemporary tradition of highly structured stochastic systems. This strategy for building complex global models through local specifications guaranteed to be self-consistent has made a huge impact on stochastic modelling in many areas of science, medicine and technology, and stimulated important work on statistical inference for such systems, and on their probabilistic theory.

The second major plank to the methodological side of his research was the innovatory work on inferential methods for spatial systems, and their computational implementation. Especially noteworthy are the notion of pseudolikelihood in interacting systems, as a computationally tractable alternative to the true likelihood, the 'iterated conditional modes' algorithm, important contributions to the algebra of interacting systems and his role as one of the very early proponents of Markov chain Monte Carlo methods for fitting statistical models. In particular, he was well ahead of his time in recognising the duality between the conditional specification of stochastic models and the construction of algorithms for such models using these conditional specifications.

Keywords: Spatial statistics; Conditional modelling; Markov chain Monte Carlo; Pseudolikelihood

Biography: Peter Green has been Professor of Statistics at the University of Bristol since 1989, and is a former colleague and collaborator of Julian Besag.