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We discuss a class of binary parametric families with conditionalprobabilities taking the form of generalized linear models and show that thisapproach allows to model high-dimensional random binary vectors with arbitrarymean and correlation. We derive the special case of logistic conditionals as anapproximation to the Ising-type exponential distribution and provide empiricalevidence that this parametric family indeed outperforms competing approaches interms of feasible correlations.

On parametric families for sampling binary data with specified mean and correlation