Non-parametric Bayesian Method Development

National Institute Of Environmental Health Sciences

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Abstract

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.