Mposterior-package: Robust and Scalable Bayes via a Median of Subset Posterior...

In Mposterior: Mposterior: R package for Robust and Scalable Bayes via a Median of Subset Posterior Measures

Description

Implementation of Weiszfeld algorithm for estimating M-posterior for robust and scalable Bayesian inference (see Minsker et al., 2014).

Details

Package:	Mposterior
Type:	Package
Version:	0.1.1
Date:	2014-05-31
License:	GPL (>= 3)
LazyLoad:	yes

findWeiszfeldMedian is the workhorse function that estimates M-posterior given samples from the subset posteriors using the Weiszfeld algorithm of Minsker et al. (2014). M-posterior is the median of subset posteriors in the space of probability measures.

Author(s)

Sanvesh Srivastava [e-mail]

References

Minsker, S., Srivastava, S., Lin, L., and Dunson, D.B. (2014). Robust and Scalable Bayes via a Median of Subset Posterior Measures. http://arxiv.org/abs/1403.2660

See Also

findWeiszfeldMedian

Examples

set.seed(12345)
## list that contains subset posterior samples from 2-dim Gaussian density
subAtomList <- vector("list", 5)
subAtomList[[1]] <- cbind(rnorm(100, mean = 1),  rnorm(100, mean = 1))
subAtomList[[2]] <- cbind(rnorm(100, mean = -1),  rnorm(100, mean= -1))
subAtomList[[3]] <- cbind(rnorm(100, mean = -1),  rnorm(100, mean = 1))
subAtomList[[4]] <- cbind(rnorm(100, mean = 1),  rnorm(100, mean = -1))
subAtomList[[5]] <- cbind(rnorm(100, mean = 2),  rnorm(100, mean = 2))
library(Mposterior)
medPosterior <- findWeiszfeldMedian(subAtomList, sigma = 0.1, maxit = 100, tol = 1e-10)
medPosterior
summary(medPosterior)
plot(medPosterior)

Example output

Loading required package: Rcpp
## 
## Mposterior: R package for Robust and Scalable Bayes via a Median of Subset Posterior Measures
## (Version 0.1.2, built: 2014-06-01)
## Copyright (C) 2013-2019 Sanvesh Srivastava
Weiszfeld iteration: 10
Weiszfeld iteration: 20
Weiszfeld iteration: 30
Weiszfeld iteration: 40
Weiszfeld algorithm coverged at iteration: 48
Subset posteriors: 
  Subset Natoms WeiszfeldWts
1      1    100         0.34
2      2    100         0.14
3      3    100         0.16
4      4    100         0.19
5      5    100         0.17
M-posterior has 2 dimensions
RBF kernel with sigma= 0.1
Weiszfeld algorithm converged in 48 iterations
Subset posteriors: 
  Subset Natoms WeiszfeldWts
1      1    100         0.34
2      2    100         0.14
3      3    100         0.16
4      4    100         0.19
5      5    100         0.17
M-posterior has 2 dimensions
RBF kernel with sigma = 0.1
Weiszfeld algorithm converged in 48 iterations

Mposterior documentation built on May 29, 2017, 6:38 p.m.