Non-parametric Bayesian Method Development

随着大型数据集的日益复杂，需要开发新的统计技术，以创建信息量大的分析，使科学家和决策者能够利用数据来源的丰富性。这项研究试图开发从噪声中提取有意义信号的方法，方法是调查一类统计技术，这些技术在分析之前没有假设响应的形式(例如，它们是非参数的)，但允许研究人员通过使用贝叶斯方法在分析中利用其他信息(可能是定性的)。虽然这项研究不是要集中在单一数据流和/或分析上，但可能的方法学发展可能包括高通量基因生物分析、空间统计建模、神经成像以及与NIEHS任务相关的其他生物数据。 本项目涉及人类冠状病毒、新型冠状病毒、新冠肺炎、严重急性呼吸综合征冠状病毒病、SARS冠状病毒、SARS冠状病毒2、SARS冠状病毒2、SARS相关冠状病毒2、SARS冠状病毒2、SARS相关冠状病毒、SARS冠状病毒或SARS相关冠状病毒的研究。

With the increasing complexity of large datasets, the development of novel statistical techniques that can create informative analyses, which allow scientists and decision makers, to leverage the richness of the data source is needed. This research seeks to develop methodologies that extract meaningful signal from the noise by investigating a class of statistical techniques that assume no form on the response prior to the analysis (e.g., they are non-parametric), but allow researchers to leverage other information (possibly qualitative) into the analysis by using Bayesian methodologies. Though this research is not meant to focus on a single data-stream and/or analysis, possible methodological developments may include analyses of high throughput gene bioassays, spatial statistical modeling, neural imaging, as well as other biologic data relevant to the mission of NIEHS. This project involves research on human coronavirus, novel coronavirus, COVID-19, Severe Acute Respiratory Syndrome coronavirus disease, SARS coronavirus, SARS-coronavirus-2, SARS-cov-2, SARS-cov2, SARS-related coronavirus 2, Severe acute respiratory syndrome coronavirus 2, SARS-Associated Coronavirus, SARS-cov, or SARS-Related Coronavirus.