plot.sbm

image.sbm

an <code><a rd-options="" href="/link/sbm?package=SBMSplitMerge&version=1.1.1" data-mini-rdoc="SBMSplitMerge::sbm">sbm</a></code> object

colours for each block - if missing, <code>rainbow</code> is used

plot an <code><a rd-options="" href="/link/sbm?package=SBMSplitMerge&version=1.1.1" data-mini-rdoc="SBMSplitMerge::sbm">sbm</a></code> object as an <code>image</code>

Inference in a Bayesian framework for a generalised stochastic block model. The generalised stochastic block model (SBM) can capture group structure in network data without requiring conjugate priors on the edge-states. Two sampling methods are provided to perform inference on edge parameters and block structure: a split-merge Markov chain Monte Carlo algorithm and a Dirichlet process sampler. Green, Richardson (2001) <doi:10.1111/1467-9469.00242>; Neal (2000) <doi:10.1080/10618600.2000.10474879>; Ludkin (2019) <arXiv:1909.09421>.

Matthew Ludkin

SBMSplitMerge

Inference for a Generalised SBM with a Split Merge Sampler

plot.sbm function

an <code><a rd-options='' href='sbm'>sbm</a></code> object

plot an <code><a rd-options='' href='sbm'>sbm</a></code> object as an <code>image</code>

plot.sbm: Plot for `sbm` object

Description

Usage

Arguments

See Also