's weights define an exponential family.

We include some additional preliminary background information.

The aim is to detect the change-point with minimal delay under constraints on false detections.

Lacking closed form expressions for the context vector, we use numerical integration: we prove exponential convergence for both families.

's weights define an exponential family.

Before proving Theorem 1, we will argue regularity. We will establish the three conditions: symmetry, shift-invariance, and monotonicity. Pr[M( q) = r] = Pr[M(Π q) = π(r)], which implies M is symmetric as desired. We first prove two lemmas. The second lemma gives a useful fact about partial sums of a non-decreasing sequence.

A Markov logic network (MLN) determines a probability distribution on the set of structures, or ``possible worlds'', with an arbitrary finite domain. We study the properties of such distributions as the domain size tends to infinity. Three types of concrete examples of MLNs will be considered, and the properties of random structures with domain sizes tending to infinity will be studied: (1) Arbitrary quantifier-free MLNs over a language with only one relation symbol which has arity 1. In this case we give a pretty complete characterization of the possible limit behaviours of random structures. (2) An MLN that favours graphs with fewer triangles (or more generally, fewer k-cliques). As a corollary of the analysis a ``$δ$-approximate 0-1 law'' for first-order logic is obtained. (3) An MLN that favours graphs with fewer vertices with degree higher than a fixed (but arbitrary) number. The analysis shows that depending on which ``soft constraints'' an MLN uses the limit behaviour of random structures can be quite different, and the weights of the soft constraints may, or may not, have influence on the limit behaviour. It will also be demonstrated, using (1), that quantifier-free MLNs and lifted Bayesian networks (in a broad sense) are asymptotically incomparable, roughly meaning that there is a sequence of distributions on possible worlds with increasing domain sizes that can be defined by one of the formalisms but not even approximated by the other. In a rather general context it is also shown that on large domains the distribution determined by an MLN concentrates almost all its probability mass on a totally different part of the space of possible worlds than the uniform distribution does.