5 papers
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…
Convergence of Multi-Level Markov Chain Monte Carlo Adaptive Stochastic Gradient Algorithms
Antoine Godichon-Baggioni, Gabriel Lang, Sylvain Le Corff +2
Stochastic optimization in learning and inference often relies on Markov chain Monte Carlo (MCMC) to approximate gradients when exact computation is intractable. However, finite-ti…
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…
Estimating Marginal Likelihoods in Likelihood-Free Inference via Neural Density Estimation
Paul Bastide, Arnaud Estoup, Jean-Michel Marin +1
The marginal likelihood, or evidence, plays a central role in Bayesian model selection, yet remains notoriously challenging to compute in likelihood-free settings. While Simulation…
Importance sampling-based gradient method for dimension reduction in Poisson log-normal model
Bastien Batardière, Julien Chiquet, Joon Kwon +1
High-dimensional count data poses significant challenges for statistical analysis, necessitating effective methods that also preserve explainability. We focus on a low rank constra…