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Convergence and asymptotic normality of variational Bayesian approximations for exponential family models with missing values

机译:具有缺失值的指数族模型的变分贝叶斯逼近的收敛性和渐近正态性

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We study the properties of variational Bayes approximations for exponential family models with missing values. It is shown that the iterative algorithm for obtaining the variational Bayesian estimator converges locally to the true value with probability 1 as the sample size becomes indefinitely large. Moreover, the variational posterior distribution is proved to be asymptotically normal.
机译:我们研究缺失值的指数族模型的变分贝叶斯近似的性质。结果表明,随着样本大小无限大,用于获得变分贝叶斯估计量的迭代算法以概率1局部收敛到真实值。此外,后验变化分布被证明是渐近正态的。

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