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，以创建信息分析，使科学家和决策者能够利用丰富的数据源。 本研究旨在通过研究一类在分析之前对响应没有任何形式的统计技术（例如，它们是非参数的），但允许研究人员利用其他信息（可能是定性的）通过使用贝叶斯方法进行分析。 虽然这项研究并不意味着集中在一个单一的数据流和/或分析，可能的方法发展可能包括高通量基因生物测定，空间统计建模，神经成像，以及其他相关的NIEHS的使命生物数据的分析。 本项目涉及人类冠状病毒、新型冠状病毒、COVID-19、严重急性呼吸综合征冠状病毒病、SARS冠状病毒、SARS-coronavirus-2、SARS-cov-2、SARS-cov 2、SARS-related coronavirus 2、严重急性呼吸综合征冠状病毒2、SARS-Associated Coronavirus、SARS-cov或SARS-related Coronavirus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.