The uncertainty underlying real-world phenomena has attracted attention toward statistical
analysis approaches. In this regard, many problems can be modeled as networks. Thus, the …

Exponential family random graph models (ERGM) are increasingly used in the study of
social networks. These models are build to explain the global structure of a network while …

Multivariate analysis using graphical models is rapidly gaining ground in psychology. In
particular, Markov Random Field (MRF) graphical models have become popular because …

A new modelling approach for the analysis of weighted networks with ordinal/polytomous
dyadic values is introduced. Specifically, it is proposed to model the weighted network …

The brain can be modelled as a network with nodes and edges derived from a range of
imaging modalities: the nodes correspond to spatially distinct regions and the edges to the …

Deriving Bayesian inference for exponential random graph models (ERGMs) is a
challenging “doubly intractable” problem as the normalizing constants of the likelihood and …

Finite Mixtures of ERGMs for Modeling Ensembles of Networks Page 1 Bayesian Analysis (2022)
17, Number 4, pp. 1153–1191 Finite Mixtures of ERGMs for Modeling Ensembles of Networks …

Modern statistical software and machine learning libraries are enabling semi-automated
statistical inference. Within this context, it appears easier and easier to try and fit many …

Recent advances in computational methods for intractable models have made network data
increasingly amenable to statistical analysis. Exponential random graph models (ERGMs) …

The Ising model is one of the most popular models in network psychometrics. However,
statistical analysis of the Ising model is difficult due to the presence of its intractable …