203 citations · 1.6k across the 78 of their papers we have counts for
13 papers · 1 filter
Amortised adaptive importance sampling
Arran Carter, Víctor Elvira, Esmeralda S. Whitammer
Monte Carlo sampling algorithms aim to produce samples from a target distribution with a given unnormalised probability density function. The high cost of running Monte Carlo algor…
When Do Differentially Private Inputs Protect Graph Shift Operators?
Andrew Campbell, Chenyue Zhang, Hang Liu +3
We study the differential privacy (DP) of a graph shift operator (GSO) when an analyst observes the output of a graph filter. In particular, we study the setting in which the input…
Repulsive normalizing flow mixtures for adaptive importance sampling: reliability analysis of complex systems
Sara Helal, Victor Elvira
Accurate rare-event estimation can be computationally expensive. Classical adaptive importance sampling (IS) schemes often rely on restrictive proposal families and can struggle un…
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…
Importance sampling for Bayesian inference: polynomial-dimension dependent error bounds
Fabián González, Víctor Elvira, Joaquín Míguez
Many Bayesian inference problems involve high-dimensional models where the performance of standard importance sampling (IS) methods often degrades rapidly as the dimensionality inc…
Truncated Neural Likelihood Estimation for Simulation-Based Inference in State-Space Models
Kostas Tsampourakis, Víctor 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…
A Gaussian Sum Filter for Unifying Gaussian and Particle Filters
Kostas Tsampourakis, Víctor Elvira
State-space models (SSMs) are a broad class of probabilistic models for dynamical systems with many applications in engineering and science. Bayesian filtering is analytically trac…
How to Approximate Inference with Subtractive Mixture Models
Lena Zellinger, Nicola Branchini, Lennert De Smet +3
Classical mixture models (MMs) are widely used tractable proposals for approximate inference settings such as variational inference (VI) and importance sampling (IS). Recently, mix…
Effective sample size approximations as entropy measures
L. Martino, V. Elvira
In this work, we analyze alternative effective sample size (ESS) metrics for importance sampling algorithms, and discuss a possible extended range of applications. We show the rela…
Efficient Plug-and-Play method for Dynamic Imaging Via Kalman Smoothing
Benjamin Hawkes, Mike Davies, Victor Elvira +1
State-space models (SSM) are common in signal processing, where Kalman smoothing (KS) methods are state-of-the-art. However, traditional KS techniques lack expressivity as they do…
Signature-Kernel Based Evaluation Metrics for Robust Probabilistic and Tail-Event Forecasting
Benjamin R. Redhead, Thomas L. Lee, Peng Gu +2
Probabilistic forecasting is increasingly critical across high-stakes domains, from finance and epidemiology to climate science. However, current evaluation frameworks lack a conse…
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…