29 citations · 59 across the 8 of their papers we have counts for
13 papers
Inferring the dependence graph density of binary graphical models in high dimension
Julien Chevallier, Eva Löcherbach, Guilherme Ost
We consider a system of binary interacting chains describing the dynamics of a group of components that, at each time unit, either send some signal to the others or remain sile…
Neural Coding as a Statistical Testing Problem
Guilherme Ost, Patricia Reynaud-Bouret
We take the testing perspective to understand what the minimal discrimination time between two stimuli is for different types of rate coding neurons. Our main goal is to describe t…
Sparse Markov Models for High-dimensional Inference
Guilherme Ost, Daniel Takahashi
Finite order Markov models are theoretically well-studied models for dependent discrete data. Despite their generality, application in empirical work when the order is large is rar…
Retrieving the structure of probabilistic sequences of auditory stimuli from EEG data
Noslen Hernández, Aline Duarte, Guilherme Ost +3
Using a new probabilistic approach we model the relationship between sequences of auditory stimuli generated by stochastic chains and the electroencephalographic (EEG) data acquire…
Fluctuations for Spatially Extended Hawkes Processes
Julien Chevallier, Guilherme Ost
In a previous paper, it has been shown that the mean-field limit of spatially extended Hawkes processes is characterized as the unique solution of a neural field equation…
Sparse space-time models: Concentration Inequalities and Lasso
Guilherme Ost, Patricia Reynaud-Bouret
Inspired by Kalikow-type decompositions, we introduce a new stochastic model of infinite neuronal networks, for which we establish sharp oracle inequalities for Lasso methods and r…