9 citations · 18 across the 5 of their papers we have counts for
6 papers
Functional Estimation of the Marginal Likelihood
Omiros Papaspiliopoulos, Timothée Stumpf-Fétizon, Jonathan Weare
We propose a framework for computing, optimizing and integrating with respect to a smooth marginal likelihood in statistical models that involve high-dimensional parameters/latent…
Scalable Bernoulli Factory MCMC for Intractable Marginalised Posteriors
Timothée Stumpf-Fétizon, Flávio B. Gonçalves
Bernoulli factory MCMC algorithms implement accept-reject Markov chains without explicit computation of acceptance probabilities, and are used to target posterior distributions ass…
Exact Bayesian inference for Markov switching diffusions
Timothée Stumpf-Fétizon, Krzysztof Łatuszyński, Jan Palczewski +1
We develop the first exact Bayesian methodology for the problem of inference in discretely observed regime switching diffusions. Switching diffusion models extend ordinary diffusio…
MCMC for multi-modal distributions
Krzysztof Łatuszyński, Matthew T. Moores, Timothée Stumpf-Fétizon
We explain the fundamental challenges of sampling from multimodal distributions, particularly for high-dimensional problems. We present the major types of MCMC algorithms that are…
Scalable Bayesian computation for crossed and nested hierarchical models
Omiros Papaspiliopoulos, Timothée Stumpf-Fétizon, Giacomo Zanella
We develop sampling algorithms to fit Bayesian hierarchical models, the computational complexity of which scales linearly with the number of observations and the number of paramete…
Bayesian forecasting of electoral outcomes with new parties' competition
José García Montalvo, Omiros Papaspiliopoulos, Timothée Stumpf-Fétizon
This paper proposed a methodology to forecast electoral outcomes using the result of the combination of a fundamental model and a model-based aggregation of polls. We propose a Bay…