dedges.numeric

a matrix of parameters (with same size as <code>edges$E</code>)

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/edgemod?package=SBMSplitMerge&version=1.1.1" data-mini-rdoc="SBMSplitMerge::edgemod">edgemod</a></code> object

edgemod

na.rm

additional arguments passed to <code>edgemod$logd</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

dedges.numeric function

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

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

dedges.numeric: likelihood of edges

Description

Usage

Arguments

Value