graphical_evidence_rmatrix {graphicalEvidence}R Documentation

Compute Marginal Likelihood using Graphical Evidence for Wishart, BGL, and GHS

Description

Computes the marginal likelihood of input data xx under one of the following priors: Wishart, Bayesian Graphical Lasso (BGL), and Graphical Horseshoe (GHS), specified under prior_name.

Usage

graphical_evidence_rmatrix(
  xx,
  burnin,
  nmc,
  prior_name = c("Wishart", "BGL", "GHS"),
  lambda = 0,
  alpha = 0,
  V = 0,
  print_progress = FALSE
)

Arguments

xx

The input data specified by a user for which the marginal likelihood is to be calculated. This should be input as a matrix like object with each individual sample of xx representing one row

burnin

The number of iterations the MCMC sampler should iterate through and discard before beginning to save results

nmc

The number of samples that the MCMC sampler should use to estimate marginal likelihood

prior_name

The name of the prior for which the marginal should be calculated, this is one of 'Wishart', 'BGL', 'GHS'

lambda

A number specifying lambda for the priors of 'BGL' and 'GHS' prior

alpha

A number specifying alpha for the priors of 'Wishart'

V

The scale matrix when specifying 'Wishart'

print_progress

A boolean which indicates whether progress should be displayed on the console as each row of the telescoping sum is computed

Value

An estimate for the marginal likelihood under specified prior with the specified parameters

Examples

# Compute the marginal likelihood of xx for GHS prior using 1,000 
# burnin and 5,000 sampled values at each call to the MCMC sampler
g_params <- gen_params_evidence('GHS')
marginal_results <- graphical_evidence_rmatrix(
  g_params$x_mat, 1e3, 5e3, 'GHS', lambda=1
)

[Package graphicalEvidence version 1.1 Index]