Model choice plays an increasingly important role in statistics. From a Bayesian perspective
a crucial goal is to compute the marginal likelihood of the data for a given model. However …

Exponential random graph models are extremely difficult models to handle from a statistical
viewpoint, since their normalising constant, which depends on model parameters, is …

Monte Carlo algorithms often aim to draw from a distribution π π by simulating a Markov
chain with transition kernel PP such that π π is invariant under P P. However, there are many …

A large number of statistical models are “doubly-intractable”: the likelihood normalising term,
which is a function of the model parameters, is intractable, as well as the marginal likelihood …

Undirected graphical models are widely used in statistics, physics, and machine vision.
However, Bayesian parameter estimation for undirected models is extremely challenging …

We propose a model based on discrete latent variables, which are spatially associated and
time specific, for the analysis of incident cases of SARS-CoV-2 infections. We assume that …

Developing satisfactory methodology for the analysis of Markov random field is a very
challenging task. Indeed, due to the Markovian dependence structure, the normalizing …

Hidden Markov random field models provide an appealing representation of images and
other spatial problems. The drawback is that inference is not straightforward for these …

The k-nearest-neighbor (knn) procedure is a well-known deterministic method used in
supervised classification. This article proposes a reassessment of this approach as a …

Exponential random graph models are an important tool in the statistical analysis of data.
However, Bayesian parameter estimation for these models is extremely challenging, since …