Package: PosteriorBootstrap 0.1.3
James Robinson
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" <arxiv:1806.11544>.
Authors:
PosteriorBootstrap_0.1.3.tar.gz
PosteriorBootstrap_0.1.3.zip(r-4.5)PosteriorBootstrap_0.1.3.zip(r-4.4)PosteriorBootstrap_0.1.3.zip(r-4.3)
PosteriorBootstrap_0.1.3.tgz(r-4.4-any)PosteriorBootstrap_0.1.3.tgz(r-4.3-any)
PosteriorBootstrap_0.1.3.tar.gz(r-4.5-noble)PosteriorBootstrap_0.1.3.tar.gz(r-4.4-noble)
PosteriorBootstrap_0.1.3.tgz(r-4.4-emscripten)PosteriorBootstrap_0.1.3.tgz(r-4.3-emscripten)
PosteriorBootstrap.pdf |PosteriorBootstrap.html✨
PosteriorBootstrap/json (API)
NEWS
| # Install 'PosteriorBootstrap' in R: |
| install.packages('PosteriorBootstrap', repos = c('https://alan-turing-institute.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/alan-turing-institute/posteriorbootstrap/issues
Last updated 11 months agofrom:c7c370b9b4
Exports:draw_logit_samplesdraw_stick_breaksget_fileget_german_credit_datasetget_german_credit_fileget_stan_file
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| A package with a parallel approach for adaptive non-parametric learning | PosteriorBootstrap-package PosteriorBootstrap |
| Draw adaptive non-parametric learning samples for logistic regression | draw_logit_samples |
| Draw stick-breaks depending on a concentration parameter | draw_stick_breaks |
| Get a file from extdata by name | get_file |
| Load and pre-process the dataset that ships with the package | get_german_credit_dataset |
| Get the file with the German Statlog credit dataset | get_german_credit_file |
| Get the Stan file with Bayesian Logistic Regression | get_stan_file |
