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跨多个受试者整合图像信息AI 解读

This article introduces a Bayesian hierarchical model for combining information across multiple images. Our work was motivated by an invasive functional brain mapping technique called direct cortical electrical interference that gives a sparse set of binary observations of an underlying “true” region at multiple sites on the brain surface. To model region shapes that may vary widely across individuals, we use mixtures of simple templates, for example, circles. These…

多项重复测量与双变量事件时间联合分析的潜变量模型AI 解读

This article presents a novel approach to analyzing a complex dataset from a prevention trial, where outcomes comprise multiple repeated mental health items and times to initiation of alcohol and tobacco use. The dataset has a nonnegligible portion of missing values and interval or left censored events. The substantive interest of the trial suggests a psychiatric distress latent variable that is reflected in the mental health items and potentially affects initiation of…

含随机效应的广义线性模型：Gibbs 抽样方法AI 解读

Generalized linear models have unified the approach to regression for a wide variety of discrete, continuous, and censored response variables that can be assumed to be independent across experimental units. In applications such as longitudinal studies, genetic studies of families, and survey sampling, observations may be obtained in clusters. Responses from the same cluster cannot be assumed to be independent. With linear models, correlation has been effectively modeled by…