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 …

Bayesian inference for models with intractable likelihood functions represents a challenging
suite of problems in modern statistics. In this work we analyse the Conway-Maxwell-Poisson …

Probabilistic graphs are challenging to visualize using the traditional node-link diagram.
Encoding edge probability using visual variables like width or fuzziness makes it difficult for …

Sectoral shocks compound via intersectoral production networks into sizable aggregate
effects. Current evidence of this mechanism has relied on country-specific data using …

Many statistical models are given in the form of non-normalized densities with an intractable
normalization constant. Since maximum likelihood estimation is computationally intensive …

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

Abstract Analysis of the structure of biological networks often uses statistical tests to
establish the over-representation of motifs, which are thought to be important building blocks …

A fragmented US healthcare delivery system may reflect a highly brokered communication
network controlled by only a few brokers. Such relational inequality in brokerage influences …

Approximate Bayesian computation (ABC) is now an established technique for statistical
inference used in cases where the likelihood function is computationally expensive or not …

Burials provide valuable information to study social structures based on the assumption that
burials and associated grave goods can represent social roles and relations in a society. To …