evidence: Wrapper function for evidence estimation

In acaimo/Bergm: Bayesian Exponential Random Graph Models

Wrapper function for evidence estimation

Description

Function to estimate the evidence (marginal likelihood) with Chib and Jeliazkov's method or Power posteriors, based on the adjusted pseudolikelihood function.

Usage

evidence(evidence.method = c("CJ", "PP"), ...)

Arguments

evidence.method	vector Method to estimate the marginal likelihood. Options are: "CJ", in which case the marginal likelihood is estimated with Chib and Jeliazkov's method; "PP", in which case the marginal likelihood is estimated with Power posteriors.
...	further arguments to be passed. See evidenceCJ and evidencePP.

References

Bouranis, L., Friel, N., & Maire, F. (2018). Bayesian model selection for exponential random graph models via adjusted pseudolikelihoods. Journal of Computational and Graphical Statistics, 27(3), 516-528. https://arxiv.org/abs/1706.06344

Examples

## Not run: 
# Load the florentine marriage network:
data(florentine)
                                                
# MCMC sampling and evidence estimation:
CJE <- evidence(evidence.method = "CJ",
                formula     = flomarriage ~ edges + kstar(2),
                main.iters  = 30000,
                burn.in     = 2000,
                aux.iters   = 1000,
                num.samples = 25000,
                V.proposal  = 2.5,
                ladder      = 100,
                seed        = 1)
                                   
# Posterior summaries:
summary(CJE)

# MCMC diagnostics plots:
plot(CJE)
    
# Log-evidence (marginal likelihood) estimate:
CJE$log.evidence

## End(Not run)

acaimo/Bergm documentation built on Jan. 17, 2024, 2:36 p.m.