This paper focuses on the relation between Gibbs and Markov random fields, one
instance of the close relation between abstract and applied mathematics so often
stressed by Lucio Russo in his scientific work.
We start by proving a more explicit version, based on spin products, of the
Hammersley–Clifford theorem, a classic result which identifies Gibbs and Markov fields
under finite energy. Then we argue that the celebrated counterexample of Moussouris,
intended to show that there is no complete coincidence between Markov and Gibbs random
fields in the presence of hard-core constraints, is not really such. In fact, the notion of a
constrained Gibbs random field used in the example and in the subsequent literature makes
the unnatural assumption that the constraints are infinite energy Gibbs interactions on the
same graph. Here we consider the more natural extended version of the equivalence problem,
in which constraints are more generally based on a possibly larger graph, and solve it.
The bearing of the more natural approach is shown by considering identifiability
of discrete random fields from support, conditional independencies and corresponding
moments. In fact, by means of our previous results, we show identifiability for a large
class of problems, and also examples with no identifiability. Various open questions
surface along the way.
Keywords
Gibbs distributions, Markov random fields, hard-core
constraints, moments, Hammersley–Clifford, Moussouris