no code implementations • 5 Jun 2023 • Alexander Lin, Bahareh Tolooshams, Yves Atchadé, Demba Ba
Latent Gaussian models have a rich history in statistics and machine learning, with applications ranging from factor analysis to compressed sensing to time series analysis.
1 code implementation • 14 Aug 2021 • Yves Atchadé, LiWei Wang
We propose a very fast approximate Markov Chain Monte Carlo (MCMC) sampling framework that is applicable to a large class of sparse Bayesian inference problems, where the computational cost per iteration in several models is of order $O(ns)$, where $n$ is the sample size, and $s$ the underlying sparsity of the model.
no code implementations • 17 Jun 2013 • Anne-Marie Lyne, Mark Girolami, Yves Atchadé, Heiko Strathmann, Daniel Simpson
The methodology is reviewed on well-known examples such as the parameters in Ising models, the posterior for Fisher-Bingham distributions on the $d$-Sphere and a large-scale Gaussian Markov Random Field model describing the Ozone Column data.