6 citations · 19 across the 9 of their papers we have counts for
4 papers · 1 filter
Estimating the inverse trace using random forests on graphs
Simon Barthelmé, Nicolas Tremblay, Alexandre Gaudillière +2
Some data analysis problems require the computation of (regularised) inverse traces, i.e. quantities of the form $\Tr (q \bI + \bL)^{-1}$. For large matrices, direct methods are un…
Divide and conquer in ABC: Expectation-Progagation algorithms for likelihood-free inference
Simon Barthelmé, Nicolas Chopin, Vincent Cottet
ABC algorithms are notoriously expensive in computing time, as they require simulating many complete artificial datasets from the model. We advocate in this paper a "divide and con…
Expectation Propagation in the large-data limit
Guillaume Dehaene, Simon Barthelmé
Expectation Propagation (Minka, 2001) is a widely successful algorithm for variational inference. EP is an iterative algorithm used to approximate complicated distributions, typica…
Expectation-Propagation for Likelihood-Free Inference
Simon Barthelmé, Nicolas Chopin
Many models of interest in the natural and social sciences have no closed-form likelihood function, which means that they cannot be treated using the usual techniques of statistica…