Bergm: evidence – R documentation

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evidence

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)

Bergm

Bayesian Exponential Random Graph Models

v5.0.2

GPL (>= 2)

Authors

Alberto Caimo [aut, cre], Lampros Bouranis [aut], Robert Krause [aut] Nial Friel [ctb]

Initial release

2020-11-12