Bayesian Nonparametrics and Biostatistics: The Case of PET Imaging.

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Mame Diarra Fall

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Mame Diarra Fall: Laboratoire de mathematiques analyse probabilites modelisation d'Orleans, Orleans45067, France.

PMID: 31774734 DOI: 10.1515/ijb-2017-0099

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.

Bayesian nonparametric Markov Chain Monte Carlo PET imaging inverse problems

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Algorithms

Bayes Theorem

Biostatistics

Brain

Humans

Image Interpretation, Computer-Assisted

Imaging, Three-Dimensional

Models, Statistical

Positron-Emission Tomography

Statistics, Nonparametric

Journal Article

OpenLB
Open Library of Bioscience