An implementation of a non-parametric statistical model using a parallelised Monte Carlo sampling scheme. The method implemented in this package allows non-parametric inference to be regularized for small sample sizes, while also being more accurate than approximations such as variational Bayes. The concentration parameter is an effective sample size parameter, determining the faith we have in the model versus the data. When the concentration is low, the samples are close to the exact Bayesian logistic regression method; when the concentration is high, the samples are close to the simplified variational Bayes logistic regression. The method is described in full in the paper Lyddon, Walker, and Holmes (2018), "Nonparametric learning from Bayesian models with randomized objective functions" <doi:10.48550/arXiv.1806.11544>.
Version: 0.1.2 Imports: e1071 (≥ 1.7.1), MASS (≥ 7.3.51.1), utils (≥ 3.4.3) Suggests: BH (≥ 1.81.0), covr (≥ 3.3.0), dplyr (≥ 0.7.4), ggplot2 (≥ 3.1.1), gridExtra (≥ 2.3), knitr (≥ 1.21), lintr (≥ 1.0.3), RcppEigen (≥ 0.3.3), RcppParallel (≥ 5.1.7), rmarkdown (≥ 1.11), roxygen2 (≥ 6.1.1), rstan (≥ 2.18.2), testthat (≥ 2.0.1), tibble (≥ 2.1.1) Published: 2023-03-12 DOI: 10.32614/CRAN.package.PosteriorBootstrap Author: Simon Lyddon [aut], Miguel Morin [aut], James Robinson [aut, cre], The Alan Turing Institute [cph] Maintainer: James Robinson <james.em.robinson at gmail.com> BugReports: https://github.com/alan-turing-institute/PosteriorBootstrap/issues License: MIT + file LICENSE URL: https://github.com/alan-turing-institute/PosteriorBootstrap/ NeedsCompilation: no Language: en-GB Materials: README NEWS CRAN checks: PosteriorBootstrap results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=PosteriorBootstrap to link to this page.
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