[HTML][HTML] A review of stochastic block models and extensions for graph clustering
C Lee, DJ Wilkinson - Applied Network Science, 2019 - Springer
There have been rapid developments in model-based clustering of graphs, also known as
block modelling, over the last ten years or so. We review different approaches and …
block modelling, over the last ten years or so. We review different approaches and …
[图书][B] Model-based clustering and classification for data science: with applications in R
Cluster analysis finds groups in data automatically. Most methods have been heuristic and
leave open such central questions as: how many clusters are there? Which method should I …
leave open such central questions as: how many clusters are there? Which method should I …
Mixture models with a prior on the number of components
JW Miller, MT Harrison - Journal of the American Statistical …, 2018 - Taylor & Francis
ABSTRACT A natural Bayesian approach for mixture models with an unknown number of
components is to take the usual finite mixture model with symmetric Dirichlet weights, and …
components is to take the usual finite mixture model with symmetric Dirichlet weights, and …
Model‐based cluster analysis
Cluster analysis seeks to identify homogeneous subgroups of cases in a population. This
article provides an introduction to model‐based clustering using finite mixture models and …
article provides an introduction to model‐based clustering using finite mixture models and …
Bayesian model-based clustering procedures
JW Lau, PJ Green - Journal of Computational and Graphical …, 2007 - Taylor & Francis
This article establishes a general formulation for Bayesian model-based clustering, in which
subset labels are exchangeable, and items are also exchangeable, possibly up to covariate …
subset labels are exchangeable, and items are also exchangeable, possibly up to covariate …
A simple example of Dirichlet process mixture inconsistency for the number of components
JW Miller, MT Harrison - Advances in neural information …, 2013 - proceedings.neurips.cc
For data assumed to come from a finite mixture with an unknown number of components, it
has become common to use Dirichlet process mixtures (DPMs) not only for density …
has become common to use Dirichlet process mixtures (DPMs) not only for density …
[HTML][HTML] Comparison of criteria for choosing the number of classes in Bayesian finite mixture models
K Nasserinejad, J van Rosmalen, W de Kort… - PloS one, 2017 - journals.plos.org
Identifying the number of classes in Bayesian finite mixture models is a challenging problem.
Several criteria have been proposed, such as adaptations of the deviance information …
Several criteria have been proposed, such as adaptations of the deviance information …
Estimation and selection for the latent block model on categorical data
This paper deals with estimation and model selection in the Latent Block Model (LBM) for
categorical data. First, after providing sufficient conditions ensuring the identifiability of this …
categorical data. First, after providing sufficient conditions ensuring the identifiability of this …
Model selection and clustering in stochastic block models based on the exact integrated complete data likelihood
E Côme, P Latouche - Statistical Modelling, 2015 - journals.sagepub.com
The stochastic block model (SBM) is a mixture model for the clustering of nodes in networks.
The SBM has now been employed for more than a decade to analyze very different types of …
The SBM has now been employed for more than a decade to analyze very different types of …
Model selection for mixture models–perspectives and strategies
G Celeux, S Frühwirth-Schnatter… - Handbook of mixture …, 2019 - taylorfrancis.com
This chapter presents some of the Bayesian solutions to the different interpretations of
picking the “right” number of components in a mixture, before concluding on the ill-posed …
picking the “right” number of components in a mixture, before concluding on the ill-posed …