PosteriorBootstrap: Non-Parametric Sampling with Parallel Monte Carlo
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:
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