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PosteriorBootstrap
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]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.PosteriorBootstrap . https://github.com/alan-turing-institute/PosteriorBootstrap/issues . https://github.com/alan-turing-institute/PosteriorBootstrap/ . PosteriorBootstrap results . PosteriorBootstrap.pdf . PosteriorBootstrap_0.1.2.tar.gz . PosteriorBootstrap_0.1.2.zip . PosteriorBootstrap_0.1.2.zip . PosteriorBootstrap_0.1.2.zip . PosteriorBootstrap_0.1.2.tgz . PosteriorBootstrap_0.1.2.tgz . PosteriorBootstrap_0.1.2.tgz . PosteriorBootstrap_0.1.2.tgz . PosteriorBootstrap_0.1.2.tgz . PosteriorBootstrap_0.1.2.tgz . PosteriorBootstrap archive . https://CRAN.R-project.org/package=PosteriorBootstrap .
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 29 Mar. 2025.
Other packages that cited PosteriorBootstrap R package
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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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