203 citations · 1.6k across the 78 of their papers we have counts for
44 papers · 1 filter
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…
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…
Nested importance sampling for Bayesian inference: error bounds and the role of dimension
Fabián González, Víctor Elvira, Joaquín Miguez
Many Bayesian inference problems involve high dimensional models for which only a subset of the model variables are of actual interest. All other variables are just nuisance parame…
Efficient rare event estimation for multimodal and high-dimensional system reliability via subset adaptive importance sampling
Sara Helal, Victor Elvira
Estimating rare events in complex systems is a key challenge in reliability analysis. The challenge grows in multimodal problems, where traditional methods often rely on a small se…
Towards Adaptive Self-Normalized Importance Samplers
Nicola Branchini, Víctor Elvira
The self-normalized importance sampling (SNIS) estimator is a Monte Carlo estimator widely used to approximate expectations in statistical signal processing and machine learning. T…
Particle Hamiltonian Monte Carlo
Alaa Amri, Víctor Elvira, Amy L. Wilson
In Bayesian inference, Hamiltonian Monte Carlo (HMC) is a popular Markov Chain Monte Carlo (MCMC) algorithm known for its efficiency in sampling from complex probability distributi…
Grid Particle Gibbs with Ancestor Sampling for State-Space Models
Mary Llewellyn, Ruth King, Víctor Elvira +1
We consider the challenge of estimating the model parameters and latent states of general state-space models within a Bayesian framework. We extend the commonly applied particle Gi…
Hybrid Population Monte Carlo
Ali Mousavi, Víctor Elvira
Importance sampling (IS) is a powerful Monte Carlo (MC) technique for approximating intractable integrals, for instance in Bayesian inference. The performance of IS relies heavily…
A Proximal Newton Adaptive Importance Sampler
Víctor Elvira, Émilie Chouzenoux, O. Deniz Akyildiz
Adaptive importance sampling (AIS) algorithms are a rising methodology in signal processing, statistics, and machine learning. An effective adaptation of the proposals is key for t…
GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models
Benjamin Cox, Emilie Chouzenoux, Victor Elvira
State-space models (SSMs) are a powerful statistical tool for modelling time-varying systems via a latent state. In these models, the latent state is never directly observed. Inste…
Generalizing self-normalized importance sampling with couplings
Nicola Branchini, Víctor Elvira
An essential problem in statistics and machine learning is the estimation of expectations involving PDFs with intractable normalizing constants. The self-normalized importance samp…
Adaptive importance sampling for heavy-tailed distributions via -divergence minimization
Thomas Guilmeau, Nicola Branchini, Emilie Chouzenoux +1
Adaptive importance sampling (AIS) algorithms are widely used to approximate expectations with respect to complicated target probability distributions. When the target has heavy ta…
Sparse Bayesian Estimation of Parameters in Linear-Gaussian State-Space Models
Benjamin Cox, Victor Elvira
State-space models (SSMs) are a powerful statistical tool for modelling time-varying systems via a latent state. In these models, the latent state is never directly observed. Inste…
Gradient-based Adaptive Importance Samplers
Víctor Elvira, Emilie Chouzenoux, Ömer Deniz Akyildiz +1
Importance sampling (IS) is a powerful Monte Carlo methodology for the approximation of intractable integrals, very often involving a target probability density function. The perfo…
Graphical Inference in Linear-Gaussian State-Space Models
Víctor Elvira, Émilie Chouzenoux
State-space models (SSM) are central to describe time-varying complex systems in countless signal processing applications such as remote sensing, networks, biomedicine, and finance…
Optimized Population Monte Carlo
Víctor Elvira, Émilie Chouzenoux
Adaptive importance sampling (AIS) methods are increasingly used for the approximation of distributions and related intractable integrals in the context of Bayesian inference. Popu…
A Point Mass Proposal Method for Bayesian State-Space Model Fitting
Mary Llewellyn, Ruth King, Víctor Elvira +1
State-space models (SSMs) are commonly used to model time series data where the observations depend on an unobserved latent process. However, inference on the model parameters of a…
A principled stopping rule for importance sampling
Medha Agarwal, Dootika Vats, Víctor Elvira
Importance sampling (IS) is a Monte Carlo technique that relies on weighted samples, simulated from a proposal distribution, to estimate intractable integrals. The quality of the e…
A Survey of Monte Carlo Methods for Parameter Estimation
D. Luengo, L. Martino, M. Bugallo +2
Statistical signal processing applications usually require the estimation of some parameters of interest given a set of observed data. These estimates are typically obtained either…
Compressed Monte Carlo with application in particle filtering
Luca Martino, Víctor Elvira
Bayesian models have become very popular over the last years in several fields such as signal processing, statistics, and machine learning. Bayesian inference requires the approxim…
MCMC-driven importance samplers
F. Llorente, E. Curbelo, L. Martino +2
Monte Carlo sampling methods are the standard procedure for approximating complicated integrals of multidimensional posterior distributions in Bayesian inference. In this work, we…
Advances in Importance Sampling
Víctor Elvira, Luca Martino
Importance sampling (IS) is a Monte Carlo technique for the approximation of intractable distributions and integrals with respect to them. The origin of IS dates from the early 195…
Optimized Auxiliary Particle Filters: adapting mixture proposals via convex optimization
Nicola Branchini, Víctor Elvira
Auxiliary particle filters (APFs) are a class of sequential Monte Carlo (SMC) methods for Bayesian inference in state-space models. In their original derivation, APFs operate in an…