25 citations · 79 across the 11 of their papers we have counts for
11 papers
Optimal Sampling for Kernel Quadrature on Unbounded Domains
Edoardo Bandoni, Christian Robert, Julien Stoehr
Kernel quadrature is widely used to approximate integrals of smooth functions, with worst-case error typically decaying at the minimax rate for smoothness in dimensi…
Entropic Mirror Monte Carlo
Anas Cherradi, Yazid Janati, Alain Durmus +3
Importance sampling is a Monte Carlo method which designs estimators of expectations under a target distribution using weighted samples from a proposal distribution. When the targe…
Composite likelihood inference for the Poisson log-normal model
Julien Stoehr, Stephane S. Robin
The Poisson log-normal model is a latent variable model that provides a generic framework for the analysis of multivariate count data. Inferring its parameters can be a daunting ta…
Simulating signed mixtures
Julien Stoehr, Christian P. Robert
Simulating mixtures of distributions with signed weights proves a challenge as standard simulation algorithms are inefficient in handling the negative weights. In particular, the n…
Component-wise approximate Bayesian computation via Gibbs-like steps
Grégoire Clarté, Christian P. Robert, Robin Ryder +1
Approximate Bayesian computation methods are useful for generative models with intractable likelihoods. These methods are however sensitive to the dimension of the parameter space,…
Faster Hamiltonian Monte Carlo by Learning Leapfrog Scale: an offline randomized solution
Changye Wu, Pierre Pudlo, Christian P. Robert +1
We introduce a Hamiltonian Monte Carlo (HMC) methodology based on an offline empirical calibration of randomized leapfrog parameters. The approach, referred to as eHMC, where \text…
Noisy Hamiltonian Monte Carlo for doubly-intractable distributions
Julien Stoehr, Alan Benson, Nial Friel
Hamiltonian Monte Carlo (HMC) has been progressively incorporated within the statistician's toolbox as an alternative sampling method in settings when standard Metropolis-Hastings…
A review on statistical inference methods for discrete Markov random fields
Julien Stoehr
Developing satisfactory methodology for the analysis of Markov random field is a very challenging task. Indeed, due to the Markovian dependence structure, the normalizing constant…
Hidden Gibbs random fields model selection using Block Likelihood Information Criterion
Julien Stoehr, Jean-Michel Marin, Pierre Pudlo
Performing model selection between Gibbs random fields is a very challenging task. Indeed, due to the Markovian dependence structure, the normalizing constant of the fields cannot…
Calibration of conditional composite likelihood for Bayesian inference on Gibbs random fields
Julien Stoehr, Nial Friel
Gibbs random fields play an important role in statistics, however, the resulting likelihood is typically unavailable due to an intractable normalizing constant. Composite likelihoo…
Adaptive ABC model choice and geometric summary statistics for hidden Gibbs random fields
Julien Stoehr, Pierre Pudlo, Lionel Cucala
Selecting between different dependency structures of hidden Markov random field can be very challenging, due to the intractable normalizing constant in the likelihood. We answer th…