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使用 Jeffreys 先验的广义线性模型贝叶斯分析AI 解读

Generalized linear models (GLM's) have proved suitable for modeling various kinds of data consisting of exponential family response variables with covariates. Bayesian analysis of such data requires specification of a prior for the regression parameters in the model used. Uniform priors are very commonly used as conventional noninformative priors. We show, however, that uniform priors for GLM's can lead to improper posterior distributions thus making them undesirable.…

广义线性模型中的不完全数据AI 解读

This article examines incomplete data for the class of generalized linear models, in which incompleteness is due to partially missing covariates on some observations. Under the assumption that the missing data are missing at random, it is shown that the E step of the EM algorithm for any generalized linear model can be expressed as a weighted complete data log-likelihood when the unobserved covariates are assumed to come from a discrete distribution with finite range.…