2018
Martino, Luca; Elvira, Victor; Miguez, Joaquín; Artés-Rodríguez, Antonio; Djuric, Petar M
A Comparison Of Clipping Strategies For Importance Sampling Proceedings Article
En: 2018 IEEE Statistical Signal Processing Workshop (SSP), 2018.
Enlaces | BibTeX | Etiquetas: Bayesian inference, Importance sampling, Monte Carlo methods, Parameter estimation, Variance Reduction methods
@inproceedings{JMiguez18d,
title = {A Comparison Of Clipping Strategies For Importance Sampling},
author = {Luca Martino and Victor Elvira and Joaqu\'{i}n Miguez and Antonio Art\'{e}s-Rodr\'{i}guez and Petar M Djuric},
doi = {10.1109/SSP.2018.8450722},
year = {2018},
date = {2018-06-10},
booktitle = {2018 IEEE Statistical Signal Processing Workshop (SSP)},
keywords = {Bayesian inference, Importance sampling, Monte Carlo methods, Parameter estimation, Variance Reduction methods},
pubstate = {published},
tppubtype = {inproceedings}
}
2013
Koblents, Eugenia; Miguez, Joaquin
A Population Monte Carlo Scheme for Computational Inference in High Dimensional Spaces Proceedings Article
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}
}