Normalized bounded Lorentzian metric measure spaces are isomorphic exactly when all of their finite-sample time-separation matrix laws coincide, and three hierarchically related measured Lorentz-Gromov-Hausdorff convergence notions are built on top of that.
Distance matrices, random metrics and Urysohn space
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abstract
We introduce an universum of the Polish (=complete separable metric) space - the convex cone of distance matrices and study its geometry. It happened that the generic Polish spaces in this sense of this universum is so called Urysohn spaces defined by P.S.Urysohn in 20-th, and generic metric triple (= metric space with probability borel measure) is also Urysohn space with non-degenerated measure. We prove that the complete invariant of the metric space with measure up to measure preserving isometries is so called matrix distribution - invarinat and ergodic measure with respect to infinite symmetric group which is concentrated on the cone of distance matrices. This defined an important new class of random matrices and family of random metrics on the naturals.
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Gromov's reconstruction theorem and measured Gromov-Hausdorff convergence in Lorentzian geometry
Normalized bounded Lorentzian metric measure spaces are isomorphic exactly when all of their finite-sample time-separation matrix laws coincide, and three hierarchically related measured Lorentz-Gromov-Hausdorff convergence notions are built on top of that.