Search results for: Víctor Elvira

Items from 1 to 20 out of 38 results

article

Rethinking the Effective Sample Size

Víctor Elvira, Luca Martino, Christian P. Robert

International Statistical Review > 90 > 3 > 525 - 550

The effective sample size (ESS) is widely used in sample‐based simulation methods for assessing the quality of a Monte Carlo approximation of a given distribution and of related integrals. In this paper, we revisit the approximation of the ESS in the specific context of importance sampling. The derivation of this approximation, that we will denote as

\hat{ESS}

, is partially available in a 1992 foundational...

article

Risk and outcomes of COVID‐19 in patients with multiple sclerosis

Irene Moreno‐Torres, Virginia Meca Lallana, Lucienne Costa‐Frossard, Celia Oreja‐Guevara, more

European Journal of Neurology > 28 > 11 > 3712 - 3721

Background and purpose Limited information is available on incidence and outcomes of COVID‐19 in patients with multiple sclerosis (MS). This study investigated the risks of SARS‐CoV‐2 infection and COVID‐19‐related outcomes in patients with MS, and compared these with the general population. Methods A regional registry was created to collect data on incidence, hospitalization rates, intensive care...

article

Accelerating MCMC algorithms

Christian P. Robert, Víctor Elvira, Nick Tawn, Changye Wu

Wiley Interdisciplinary Reviews: Computational Statistics > 10 > 5 > n/a - n/a

Markov chain Monte Carlo algorithms are used to simulate from complex statistical distributions by way of a local exploration of these distributions. This local feature avoids heavy requests on understanding the nature of the target, but it also potentially induces a lengthy exploration of this target, with a requirement on the number of simulations that grows with the dimension of the problem and...

article

Multiple importance sampling characterization by weighted mean invariance

Mateu Sbert, Vlastimil Havran, László Szirmay-Kalos, Víctor Elvira

The Visual Computer > 2018 > 34 > 6-8 > 843-852

In this paper, we examine the linear combination of techniques and multiple importance sampling for Monte Carlo integration from a new perspective of quasi-arithmetic weighted means. The invariance property of these means allows us to define a new family of heuristics. We illustrate our results with several rendering examples, including environment mapping and path tracing.

article

The Recycling Gibbs sampler for efficient learning

Luca Martino, Víctor Elvira, Gustau Camps-Valls

Digital Signal Processing > 2018 > 74 > C > 1-13

Monte Carlo methods are essential tools for Bayesian inference. Gibbs sampling is a well-known Markov chain Monte Carlo (MCMC) algorithm, extensively used in signal processing, machine learning, and statistics, employed to draw samples from complicated high-dimensional posterior distributions. The key point for the successful application of the Gibbs sampler is the ability to draw efficiently samples...

chapter

Group metropolis sampling

Luca Martino, Victor Elvira, Gustau Camps-Valls

2017 25th European Signal Processing Conference (EUSIPCO) > 201 - 205

2017 25th European Signal Processing Conference (EUSIPCO)

Monte Carlo (MC) methods are widely used for Bayesian inference and optimization in statistics, signal processing and machine learning. Two well-known class of MC methods are the Importance Sampling (IS) techniques and the Markov Chain Monte Carlo (MCMC) algorithms. In this work, we introduce the Group Importance Sampling (GIS) framework where different sets of weighted samples are properly summarized...

chapter

Anti-tempered layered adaptive importance sampling

Luca Martino, Victor Elvira, David Luengo

2017 22nd International Conference on Digital Signal Processing (DSP) > 1 - 5

2017 22nd International Conference on Digital Signal Processing (DSP)

Monte Carlo (MC) methods are widely used for Bayesian inference in signal processing, machine learning and statistics. In this work, we introduce an adaptive importance sampler which mixes together the benefits of the Importance Sampling (IS) and Markov Chain Monte Carlo (MCMC) approaches. Different parallel MCMC chains provide the location parameters of the proposal probability density functions...

chapter

Recycling Gibbs sampling

Luca Martino, Victor Elvira, Gustau Camps-Valls

2017 25th European Signal Processing Conference (EUSIPCO) > 171 - 175

2017 25th European Signal Processing Conference (EUSIPCO)

Gibbs sampling is a well-known Markov chain Monte Carlo (MCMC) algorithm, extensively used in signal processing, machine learning and statistics. The key point for the successful application of the Gibbs sampler is the ability to draw samples from the full-conditional probability density functions efficiently. In the general case this is not possible, so in order to speed up the convergence of the...

article

Adaptive Importance Sampling: The past, the present, and the future

Monica F. Bugallo, Victor Elvira, Luca Martino, David Luengo, more

IEEE Signal Processing Magazine > 2017 > 34 > 4 > 60 - 79

A fundamental problem in signal processing is the estimation of unknown parameters or functions from noisy observations. Important examples include localization of objects in wireless sensor networks [1] and the Internet of Things [2]; multiple source reconstruction from electroencephalograms [3]; estimation of power spectral density for speech enhancement [4]; or inference in genomic signal processing...

article

Adapting the Number of Particles in Sequential Monte Carlo Methods Through an Online Scheme for Convergence Assessment

Victor Elvira, Joaquin Miguez, Petar M. Djurie

IEEE Transactions on Signal Processing > 2017 > 65 > 7 > 1781 - 1794

Particle filters are broadly used to approximate posterior distributions of hidden states in state-space models by means of sets of weighted particles. While the convergence of the filter is guaranteed when the number of particles tends to infinity, the quality of the approximation is usually unknown but strongly dependent on the number of particles. In this paper, we propose a novel method for assessing...

chapter

Latent variable analysis of causal interactions in atrial fibrillation electrograms

David Luengo, Victor Elvira

2016 Computing in Cardiology Conference (CinC) > 981 - 984

2016 Computing in Cardiology Conference (CinC)

Multi-channel intracardiac electrocardiograms of atrial fibrillation (AF) patients are acquired at the electrophysiology laboratory in order to guide radiofrequency (RF) ablation surgery. Unfortunately, the success rate of RF ablation is still moderate, since the mechanisms underlying AF initiation and maintenance are still not precisely known. In this paper, we use an advanced machine learning model,...

article

Improving population Monte Carlo: Alternative weighting and resampling schemes

Víctor Elvira, Luca Martino, David Luengo, Mónica F. Bugallo

Signal Processing > 2017 > 131 > C > 77-91

Population Monte Carlo (PMC) sampling methods are powerful tools for approximating distributions of static unknowns given a set of observations. These methods are iterative in nature: at each step they generate samples from a proposal distribution and assign them weights according to the importance sampling principle. Critical issues in applying PMC methods are the choice of the generating functions...

article

Effective sample size for importance sampling based on discrepancy measures

Luca Martino, Víctor Elvira, Francisco Louzada

Signal Processing > 2017 > 131 > C > 386-401

The Effective Sample Size (ESS) is an important measure of efficiency of Monte Carlo methods such as Markov Chain Monte Carlo (MCMC) and Importance Sampling (IS) techniques. In the IS context, an approximation ESS^ of the theoretical ESS definition is widely applied, involving the inverse of the sum of the squares of the normalized importance weights. This formula, ESS^, has become an essential piece...

article

Cooperative parallel particle filters for online model selection and applications to urban mobility

Luca Martino, Jesse Read, Víctor Elvira, Francisco Louzada

Digital Signal Processing > 2017 > 60 > C > 172-185

We design a sequential Monte Carlo scheme for the dual purpose of Bayesian inference and model selection. We consider the application context of urban mobility, where several modalities of transport and different measurement devices can be employed. Therefore, we address the joint problem of online tracking and detection of the current modality. For this purpose, we use interacting parallel particle...

chapter

A new strategy for effective learning in population Monte Carlo sampling

Monica F. Bugallo, Victor Elvira, Luca Martino

2016 50th Asilomar Conference on Signals, Systems and Computers > 1540 - 1544

2016 50th Asilomar Conference on Signals, Systems and Computers

In this work, we focus on advancing the theory and practice of a class of Monte Carlo methods, population Monte Carlo (PMC) sampling, for dealing with inference problems with static parameters. We devise a new method for efficient adaptive learning from past samples and weights to construct improved proposal functions. It is based on assuming that, at each iteration, there is an intermediate target...

article

Heretical Multiple Importance Sampling

Victor Elvira, Luca Martino, David Luengo, Monica F. Bugallo

IEEE Signal Processing Letters > 2016 > 23 > 10 > 1474 - 1478

Multiple importance sampling (MIS) methods approximate moments of complicated distributions by drawing samples from a set of proposal distributions. Several ways to compute the importance weights assigned to each sample have been recently proposed, with the so-called deterministic mixture (DM) weights providing the best performance in terms of variance, at the expense of an increase in the computational...

chapter

On the relationship between online Gaussian process regression and kernel least mean squares algorithms

Steven Van Vaerenbergh, Jesus Fernandez-Bes, Victor Elvira

2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP) > 1 - 6

2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP)

We study the relationship between online Gaussian process (GP) regression and kernel least mean squares (KLMS) algorithms. While the latter have no capacity of storing the entire posterior distribution during online learning, we discover that their operation corresponds to the assumption of a fixed posterior covariance that follows a simple parametric model. Interestingly, several well-known KLMS...

chapter

Adaptive population importance samplers: A general perspective

Luca Martino, Victor Elvira, David Luengo, Francisco Louzada

2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) > 1 - 5

2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)

Importance sampling (IS) is a well-known Monte Carlo method, widely used to approximate a distribution of interest using a random measure composed of a set of weighted samples generated from another proposal density. Since the performance of the algorithm depends on the mismatch between the target and the proposal densities, a set of proposals is often iteratively adapted in order to reduce the variance...

chapter

A novel algorithm for adapting the number of particles in particle filtering

Victor Elvira, Joaquin Miguez, Petar M. Djuric

2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM) > 1 - 5

2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)

In this paper, we propose a novel approach for assessing the convergence of particle filters in online manner. Particle filters sequentially approximate distributions of hidden states of state-space models. The approximations are random measures composed of weighted particles (i.e., samples of the state). A sufficiently large number of particles provides a good quality in the approximation but at...

chapter

Multiple importance sampling with overlapping sets of proposals

Victor Elvira, Luca Martino, David Luengo, Monica F. Bugallo

2016 IEEE Statistical Signal Processing Workshop (SSP) > 1 - 5

2016 IEEE Statistical Signal Processing Workshop (SSP)

In this paper, we introduce multiple importance sampling (MIS) approaches with overlapping (i.e., non-disjoint) sets of proposals. We derive a novel weighting scheme, based on the deterministic mixture methodology, that leads to unbiased estimators. The proposed framework can be seen as a generalization of other well-known MIS algorithms available in the literature. Furthermore, it allows us to achieve...

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INFONA - science communication portal

Search results for: Víctor Elvira

Rethinking the Effective Sample Size

Risk and outcomes of COVID‐19 in patients with multiple sclerosis

Accelerating MCMC algorithms

Multiple importance sampling characterization by weighted mean invariance

The Recycling Gibbs sampler for efficient learning

Group metropolis sampling

Anti-tempered layered adaptive importance sampling

Recycling Gibbs sampling

Adaptive Importance Sampling: The past, the present, and the future

Adapting the Number of Particles in Sequential Monte Carlo Methods Through an Online Scheme for Convergence Assessment

Latent variable analysis of causal interactions in atrial fibrillation electrograms

Improving population Monte Carlo: Alternative weighting and resampling schemes

Effective sample size for importance sampling based on discrepancy measures

Cooperative parallel particle filters for online model selection and applications to urban mobility

A new strategy for effective learning in population Monte Carlo sampling

Heretical Multiple Importance Sampling

On the relationship between online Gaussian process regression and kernel least mean squares algorithms

Adaptive population importance samplers: A general perspective

A novel algorithm for adapting the number of particles in particle filtering

Multiple importance sampling with overlapping sets of proposals

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