26 papers
Beyond Effective Sample Size: Effective Number of Proposals for Adaptive Importance Sampling
Ali Mousavi, Victor Elvira
Population-based adaptive importance sampling (AIS) methods use a set of proposal densities to approximate complex target distributions. Their performance is commonly assessed thro…
Scalable estimation of VARMA models
Daniel Paulin, Victor Elvira
Vector autoregressive moving-average (VARMA) models have long been considered impractical beyond moderate dimensions: the likelihood is non-convex, the parametrization is identifie…
Reinforced sequential Monte Carlo for amortised sampling
Sanghyeok Choi, Sarthak Mittal, VÃctor Elvira +2
This paper proposes a synergy of amortised and particle-based methods for sampling from distributions defined by unnormalised density functions. We state a connection between seque…
Importance sampling for Bayesian inference: polynomial-dimension dependent error bounds
Fabián González, VÃctor Elvira, JoaquÃn MÃguez
Many Bayesian inference problems involve high-dimensional models where the performance of standard importance sampling (IS) methods often degrades rapidly as the dimensionality inc…
Discrete diffusion samplers and bridges: Off-policy algorithms and applications in latent spaces
Arran Carter, Sanghyeok Choi, Kirill Tamogashev +2
Sampling from a distribution known up to a normalising constant is an important and challenging problem in statistics. Recent years have seen the…
Truncated Neural Likelihood Estimation for Simulation-Based Inference in State-Space Models
Kostas Tsampourakis, VÃctor Elvira
State-space models (SSMs) are powerful probabilistic tools for modeling time-varying systems with latent dynamics. Inference in SSMs involves the estimation of latent states and pa…