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PosteriorBootstrap  

Non-Parametric Sampling with Parallel Monte Carlo
View on CRAN: Click here


Download and install PosteriorBootstrap package within the R console
Install from CRAN:
install.packages("PosteriorBootstrap")

Install from Github:
library("remotes")
install_github("cran/PosteriorBootstrap")

Install by package version:
library("remotes")
install_version("PosteriorBootstrap", "0.1.2")



Attach the package and use:
library("PosteriorBootstrap")
Maintained by
James Robinson
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-10-09
Latest Update: 2023-03-12
Description:
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" .
How to cite:
James Robinson (2019). PosteriorBootstrap: Non-Parametric Sampling with Parallel Monte Carlo. R package version 0.1.2, https://cran.r-project.org/web/packages/PosteriorBootstrap. Accessed 15 Jul. 2026.
Previous versions and publish date:
(2026-07-09 08:17), 0.1.0 (2019-10-09 13:00), 0.1.1 (2021-05-14 15:02)
Other packages that cited PosteriorBootstrap R package
View PosteriorBootstrap citation profile
Other R packages that PosteriorBootstrap depends, imports, suggests or enhances
Complete documentation for PosteriorBootstrap
Functions, R codes and Examples using the PosteriorBootstrap R package
Some associated functions: PosteriorBootstrap . draw_logit_samples . draw_stick_breaks . get_file . get_german_credit_dataset . get_german_credit_file . get_stan_file . 
Some associated R codes: PosteriorBootstrap.R . data_loader.R . german_credit_loader.R .  Full PosteriorBootstrap package functions and examples
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