贝叶斯非参数和生物统计学：以PET成像为例

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2019-11-01 DOI:10.1515/ijb-2017-0099

Mame Diarra Fall

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引用次数: 0

摘要

摘要生物统计学应用程序通常需要收集和分析大量数据。因此，有必要考虑在表征复杂数据方面表现良好的新统计范式。非参数贝叶斯方法提供了一个广泛使用的框架，它提供了完全基于模型的概率框架的关键优势，同时具有高度的灵活性和适应性。本文的目标是通过一个特定的生物医学应用，即正电子发射断层扫描（PET）成像重建，提供贝叶斯非框架（BNP）的动机。

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Bayesian Nonparametrics and Biostatistics: The Case of PET Imaging

Abstract Biostatistic applications often require to collect and analyze a massive amount of data. Hence, it has become necessary to consider new statistical paradigms that perform well in characterizing complex data. Nonparametric Bayesian methods provide a widely used framework that offers the key advantages of a fully model-based probabilistic framework, while being highly flexible and adaptable. The goal of this paper is to provide a motivation of Bayesian nonparametrics (BNP) through a particular biomedical application, namely Positron Emission Tomography (PET) imaging reconstruction.

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来源期刊

International Journal of Biostatistics Mathematics-Statistics and Probability

CiteScore

2.30

自引率

8.30%

发文量

期刊介绍： The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.