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Bayesian Nonparametrics and Biostatistics: The Case of PET Imaging

Author

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  • Fall Mame Diarra

    (Laboratoire de mathematiques analyse probabilites modelisation d’Orleans, Orleans45067, France)

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.

Suggested Citation

  • Fall Mame Diarra, 2019. "Bayesian Nonparametrics and Biostatistics: The Case of PET Imaging," The International Journal of Biostatistics, De Gruyter, vol. 15(2), pages 1-10, November.
    • Handle: RePEc:bpj:ijbist:v:15:y:2019:i:2:p:10:n:3
    DOI: 10.1515/ijb-2017-0099
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