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Exponential-family random network (ERN) models specify a joint representation of both the dyads of a network and nodal characteristics. This class of models allow the nodal characteristics to be model...
We study the properties of variational Bayes approximations for exponential family mod-els with missing values. It is shown that the iterative algorithm for obtaining the varia-tional Bayesian estimat...
Random graphs, where the connections between nodes are considered random variables, have wide applicability in the social sciences. Exponential-family Random Graph Models (ERGM) have shown themselves ...
Limits of densities belonging to an exponential family appear in many applications, {e.g.} Gibbs models in Statistical Physics, relaxed combinatorial optimization, coding theory, critical likelihood c...
Limits of densities belonging to an exponential family appear in many applications, {e.g.} Gibbs models in Statistical Physics, relaxed combinatorial optimization, coding theory, critical likelihood c...
Proper Priors Yielding Linear Bayes Estimators for the Natural Parameter of an Exponential Family
Skewness and Kurtosis for Maximum Likelihood Estimator in One-Parameter Exponential Family Models。
This paper is devoted to the estimation of a vector θ parametrizing an energy function of a Gibbs point process, via the maximum pseudolikelihood method. Strong consistency and asymptotic normality re...

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