23 citations · 118 across the 21 of their papers we have counts for
8 papers · 1 filter
Tempered, Anti-trunctated, Multiple Importance Sampling
Grégoire Aufort, Pierre Pudlo, Denis Burgarella
Importance sampling is a Monte Carlo method that introduces a proposal distribution to sample the space according to the target distribution. Yet calibration of the proposal distri…
Combined parameter and state inference with automatically calibrated ABC
Anthony Ebert, Pierre Pudlo, Kerrie Mengersen +2
State space models contain time-indexed parameters, termed states, as well as static parameters, simply termed parameters. The problem of inferring both static parameters as well a…
Faster Hamiltonian Monte Carlo by Learning Leapfrog Scale: an offline randomized solution
Changye Wu, Pierre Pudlo, Christian P. Robert +1
We introduce a Hamiltonian Monte Carlo (HMC) methodology based on an offline empirical calibration of randomized leapfrog parameters. The approach, referred to as eHMC, where \text…
Hidden Gibbs random fields model selection using Block Likelihood Information Criterion
Julien Stoehr, Jean-Michel Marin, Pierre Pudlo
Performing model selection between Gibbs random fields is a very challenging task. Indeed, due to the Markovian dependence structure, the normalizing constant of the fields cannot…
Consistency of the Adaptive Multiple Importance Sampling
Jean-Michel Marin, Pierre Pudlo, Mohammed Sedki
Among Monte Carlo techniques, the importance sampling requires fine tuning of a proposal distribution, which is now fluently resolved through iterative schemes. The Adaptive Multip…
Efficient learning in ABC algorithms
Mohammed Sedki, Pierre Pudlo, Jean-Michel Marin +2
Approximate Bayesian Computation has been successfully used in population genetics to bypass the calculation of the likelihood. These methods provide accurate estimates of the post…
Bayesian computation via empirical likelihood
K. L. Mengersen, P. Pudlo, C. P. Robert
Approximate Bayesian computation (ABC) has become an essential tool for the analysis of complex stochastic models when the likelihood function is numerically unavailable. However,…
Approximate Bayesian Computational methods
Jean-Michel Marin, Pierre Pudlo, Christian P. Robert +1
Also known as likelihood-free methods, approximate Bayesian computational (ABC) methods have appeared in the past ten years as the most satisfactory approach to untractable likelih…