203 citations · 1.6k across the 78 of their papers we have counts for
11 papers · 1 filter
PyDPF: A Python Package for Differentiable Particle Filtering
John-Joseph Brady, Benjamin Cox, Yunpeng Li +1
State-space models (SSMs) are a widely used tool in time series analysis. In the complex systems that arise from real-world data, it is common to employ particle filtering (PF), an…
Reinforced sequential Monte Carlo for amortised sampling
Sanghyeok Choi, Sarthak Mittal, Víctor 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…
Multi-Marginal Flow Matching with Adversarially Learnt Interpolants
Oskar Kviman, Kirill Tamogashev, Nicola Branchini +3
Learning the dynamics of a process given sampled observations at several time points is an important but difficult task in many scientific applications. When no ground-truth trajec…
Differential Privacy of Network Parameters from a System Identification Perspective
Andrew Campbell, Anna Scaglione, Hang Liu +3
This paper addresses the problem of protecting network information from privacy system identification (SI) attacks when sharing cyber-physical system simulations. We model analyst…
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…
A Collaborative Platform for Soil Organic Carbon Inference Based on Spatiotemporal Remote Sensing Data
Jose Manuel Aroca-Fernandez, Jose Francisco Diez-Pastor, Pedro Latorre-Carmona +4
Soil organic carbon (SOC) is a key indicator of soil health, fertility, and carbon sequestration, making it essential for sustainable land management and climate change mitigation.…
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…
Scalable Expectation Estimation with Subtractive Mixture Models
Lena Zellinger, Nicola Branchini, Víctor Elvira +1
Many Monte Carlo (MC) and importance sampling (IS) methods use mixture models (MMs) for their simplicity and ability to capture multimodal distributions. Recently, subtractive mixt…
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…