sampler.rj: reversible jump Markov chain Monte Carlo split-merge sampler

In SBMSplitMerge: Inference for a Generalised SBM with a Split Merge Sampler

Description

reversible jump Markov chain Monte Carlo split-merge sampler

Usage

sampler.rj(currsbm, edges, sbmmod, sigma, rho = 10)

Arguments

currsbm	the current state of the sampler
edges	an edges object
sbmmod	an sbmmod model
sigma	random walk parameter for theta
rho	propensity to add a block

Value

next state of currsbm object

See Also

For full algorithm details see http://doi.org/10.17635/lancaster/thesis/296

Examples

model <- sbmmod(dma(1,10), param_nbin(1,1,4,4,0.5,0.5,0.5,0.5), edges_nbin())
trueSBM <- model$r(100)
Edges <- redges(trueSBM, model$edge)
rj_out <- sampler(Edges, model, 10, "rj", sigma=0.1)

SBMSplitMerge documentation built on July 1, 2020, 5:23 p.m.