An implementation of a nonparametric statistical model using a parallelised Monte Carlo sampling scheme. The method implemented in this package allows nonparametric 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>.
Package details 


Maintainer  
License  MIT + file LICENSE 
Version  0.1.0 
URL  https://github.com/alanturinginstitute/PosteriorBootstrap/ 
Package repository  View on GitHub 
Installation 
Install the latest version of this package by entering the following in R:

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