### 2015

Martino, Luca; Elvira, Victor; Luengo, David; Corander, Jukka

Parallel interacting Markov adaptive importance sampling Artículo en actas

En: 2015 23rd European Signal Processing Conference (EUSIPCO), pp. 499–503, IEEE, Nice, 2015, ISBN: 978-0-9928-6263-3.

Resumen | Enlaces | BibTeX | Etiquetas: Adaptive importance sampling, Bayesian inference, MCMC methods, Monte Carlo methods, Parallel Chains, Probability density function, Proposals, Signal processing, Signal processing algorithms, Sociology

@inproceedings{Martino2015bb,

title = {Parallel interacting Markov adaptive importance sampling},

author = {Luca Martino and Victor Elvira and David Luengo and Jukka Corander},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7362433 http://www.eurasip.org/Proceedings/Eusipco/Eusipco2015/papers/1570111267.pdf},

doi = {10.1109/EUSIPCO.2015.7362433},

isbn = {978-0-9928-6263-3},

year = {2015},

date = {2015-08-01},

booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},

pages = {499--503},

publisher = {IEEE},

address = {Nice},

abstract = {Monte Carlo (MC) methods are widely used for statistical inference in signal processing applications. A well-known class of MC methods is importance sampling (IS) and its adaptive extensions. In this work, we introduce an iterated importance sampler using a population of proposal densities, which are adapted according to an MCMC technique over the population of location parameters. The novel algorithm provides a global estimation of the variables of interest iteratively, using all the samples weighted according to the deterministic mixture scheme. Numerical results, on a multi-modal example and a localization problem in wireless sensor networks, show the advantages of the proposed schemes.},

keywords = {Adaptive importance sampling, Bayesian inference, MCMC methods, Monte Carlo methods, Parallel Chains, Probability density function, Proposals, Signal processing, Signal processing algorithms, Sociology},

pubstate = {published},

tppubtype = {inproceedings}

}

Elvira, Victor; Martino, Luca; Luengo, David; Corander, Jukka

A Gradient Adaptive Population Importance Sampler Artículo en actas

En: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4075–4079, IEEE, Brisbane, 2015, ISBN: 978-1-4673-6997-8.

Resumen | Enlaces | BibTeX | Etiquetas: adaptive extensions, adaptive importance sampler, Adaptive importance sampling, gradient adaptive population, gradient matrix, Hamiltonian Monte Carlo, Hessian matrices, Hessian matrix, learning (artificial intelligence), Machine learning, MC methods, Monte Carlo, Monte Carlo methods, population Monte Carlo (PMC), proposal densities, Signal processing, Sociology, statistics, target distribution

@inproceedings{Elvira2015a,

title = {A Gradient Adaptive Population Importance Sampler},

author = {Victor Elvira and Luca Martino and David Luengo and Jukka Corander},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7178737 http://www.tsc.uc3m.es/~velvira/papers/ICASSP2015_elvira.pdf},

doi = {10.1109/ICASSP.2015.7178737},

isbn = {978-1-4673-6997-8},

year = {2015},

date = {2015-04-01},

booktitle = {2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},

pages = {4075--4079},

publisher = {IEEE},

address = {Brisbane},

abstract = {Monte Carlo (MC) methods are widely used in signal processing and machine learning. A well-known class of MC methods is composed of importance sampling and its adaptive extensions (e.g., population Monte Carlo). In this paper, we introduce an adaptive importance sampler using a population of proposal densities. The novel algorithm dynamically optimizes the cloud of proposals, adapting them using information about the gradient and Hessian matrix of the target distribution. Moreover, a new kind of interaction in the adaptation of the proposal densities is introduced, establishing a trade-off between attaining a good performance in terms of mean square error and robustness to initialization.},

keywords = {adaptive extensions, adaptive importance sampler, Adaptive importance sampling, gradient adaptive population, gradient matrix, Hamiltonian Monte Carlo, Hessian matrices, Hessian matrix, learning (artificial intelligence), Machine learning, MC methods, Monte Carlo, Monte Carlo methods, population Monte Carlo (PMC), proposal densities, Signal processing, Sociology, statistics, target distribution},

pubstate = {published},

tppubtype = {inproceedings}

}

### 2013

Koblents, Eugenia; Miguez, Joaquin

A Population Monte Carlo Scheme for Computational Inference in High Dimensional Spaces Artículo en actas

En: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6318–6322, IEEE, Vancouver, 2013, ISSN: 1520-6149.

Resumen | Enlaces | BibTeX | Etiquetas: Approximation methods, computational inference, degeneracy of importance weights, high dimensional spaces, Importance sampling, importance weights, iterative importance sampling, iterative methods, mixture-PMC, mixture-PMC algorithm, Monte Carlo methods, MPMC, nonlinear transformations, population Monte Carlo, population Monte Carlo scheme, Probability density function, probability distributions, Proposals, Sociology, Standards

@inproceedings{Koblents2013a,

title = {A Population Monte Carlo Scheme for Computational Inference in High Dimensional Spaces},

author = {Eugenia Koblents and Joaquin Miguez},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6638881},

issn = {1520-6149},

year = {2013},

date = {2013-01-01},

booktitle = {2013 IEEE International Conference on Acoustics, Speech and Signal Processing},

pages = {6318--6322},

publisher = {IEEE},

address = {Vancouver},

abstract = {In this paper we address the Monte Carlo approximation of integrals with respect to probability distributions in high-dimensional spaces. In particular, we investigate the population Monte Carlo (PMC) scheme, which is based on an iterative importance sampling (IS) approach. Both IS and PMC suffer from the well known problem of degeneracy of the importance weights (IWs), which is closely related to the curse-of-dimensionality, and limits their applicability in large-scale practical problems. In this paper we investigate a novel PMC scheme that consists in performing nonlinear transformations of the IWs in order to smooth their variations and avoid degeneracy. We apply the modified IS scheme to the well-known mixture-PMC (MPMC) algorithm, which constructs the importance functions as mixtures of kernels. We present numerical results that show how the modified version of MPMC clearly outperforms the original scheme.},

keywords = {Approximation methods, computational inference, degeneracy of importance weights, high dimensional spaces, Importance sampling, importance weights, iterative importance sampling, iterative methods, mixture-PMC, mixture-PMC algorithm, Monte Carlo methods, MPMC, nonlinear transformations, population Monte Carlo, population Monte Carlo scheme, Probability density function, probability distributions, Proposals, Sociology, Standards},

pubstate = {published},

tppubtype = {inproceedings}

}