accept

currsbm

propsbm

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

edges

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

sbmmod

log Jacobian of transformation of variables

logjac

log density for auxiliary variables

logu

additional arguments to pass to <code>dedges</code>

accept <code>propsbm</code> with the acceptance probability alpha

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

accept function

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

an <code><a rd-options='' href='sbmmod'>sbmmod</a></code> model

accept: accept `propsbm` with the acceptance probability alpha

Description

Usage

Arguments

Value