5 papers
Approximate Bayesian Computation with Deep Learning and Conformal prediction
Meili Baragatti, Casenave Céline, Bertrand Cloez +2
Approximate Bayesian Computation (ABC) methods are commonly used to approximate posterior distributions in models with unknown or computationally intractable likelihoods. Classical…
A Bayesian approach for the segmentation of series corrupted by a functional part
Meili Baragatti, Karine Bertin, Emilie Lebarbier +1
We propose a Bayesian approach to detect multiple change-points in a piecewise-constant signal corrupted by a functional part corresponding to environmental or experimental disturb…
Likelihood-Free Parallel Tempering
Meili Baragatti, Agnès Grimaud, Denys Pommeret
Approximate Bayesian Computational (ABC) methods (or likelihood-free methods) have appeared in the past fifteen years as useful methods to perform Bayesian analyses when the likeli…
A study of variable selection using g-prior distribution with ridge parameter
Meili Baragatti, Denys Pommeret
In the Bayesian stochastic search variable selection framework, a common prior distribution for the regression coefficients is the g-prior of Zellner (1986). However, there are two…
Parallel Tempering with Equi-Energy Moves
Meili Baragatti, Agnès Grimaud, Denys Pommeret
The Equi-Energy Sampler (EES) introduced by Kou et al [2006] is based on a population of chains which are updated by local moves and global moves, also called equi-energy jumps. Th…