Regular conditional distributions of continuous max-infinitely divisible random fields

This paper is devoted to the prediction problem in extreme value theory. Our main result is an explicit expression of the regular conditional distribution of a max-stable (or max-infinitely divisible) process {eta(t)}(t is an element of T) given observations {eta(t(i)) = y(i); 1 <= i <= k}. Our starting point is the point process representation of max-infinitely divisible processes by Gine, Hahn and Vatan (1990). We carefully analyze the structure of the underlying point process, introduce the notions of extremal function, sub-extremal function and hitting scenario associated to the constraints and derive the associated distributions. This allows us to explicit the conditional distribution as a mixture over all hitting scenarios compatible with the conditioning constraints. This formula extends a recent result by Wang and Stoev (2011) dealing with the case of spectrally discrete max-stable random fields. This paper offers new tools and perspective for prediction in extreme value theory together with numerous potential applications.