fipp: Induced Priors in Bayesian Mixture Models

Jan Greve* (Developer), Bettina Grün (Developer), Gertraud Malsiner-Walli (Developer), Sylvia Frühwirth-Schnatter (Developer)

*Corresponding author for this work

Publication: Non-textual formSoftware


Computes implicitly induced quantities from prior/hyperparameter specifications of three Mixtures of Finite Mixtures models: Dirichlet Process Mixtures (DPMs; Escobar and West (1995) ), Static Mixtures of Finite Mixtures (Static MFMs; Miller and Harrison (2018) ), and Dynamic Mixtures of Finite Mixtures (Dynamic MFMs; Frühwirth-Schnatter, Malsiner-Walli and Grün (2020) ). For methodological details, please refer to Greve, Grün, Malsiner-Walli and Frühwirth-Schnatter (2020) ) as well as the package vignette.
Original languageEnglish
PublisherR Foundation for Statistical Computing
Publication statusPublished - 11 Feb 2021

Austrian Classification of Fields of Science and Technology (ÖFOS)

  • 101018 Statistics

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