JAVIER LOPEZ SANTIAGO - Academia.edu (original) (raw)
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Papers by JAVIER LOPEZ SANTIAGO
2020 28th European Signal Processing Conference (EUSIPCO), 2021
Monthly Notices of the Royal Astronomical Society, 2021
Model fitting is possibly the most extended problem in science. Classical approaches include the ... more Model fitting is possibly the most extended problem in science. Classical approaches include the use of least-squares fitting procedures and maximum likelihood methods to estimate the value of the parameters in the model. However, in recent years, Bayesian inference tools have gained traction. Usually, Markov chain Monte Carlo (MCMC) methods are applied to inference problems, but they present some disadvantages, particularly when comparing different models fitted to the same data set. Other Bayesian methods can deal with this issue in a natural and effective way. We have implemented an importance sampling (IS) algorithm adapted to Bayesian inference problems in which the power of the noise in the observations is not known a priori. The main advantage of IS is that the model evidence can be derived directly from the so-called importance weights ā while MCMC methods demand considerable postprocessing. The use of our adaptive target adaptive importance sampling (ATAIS) method is shown ...
Mathematics, 2021
We propose a novel adaptive importance sampling scheme for Bayesian inversion problems where the ... more We propose a novel adaptive importance sampling scheme for Bayesian inversion problems where the inference of the variables of interest and the power of the data noise are carried out using distinct (but interacting) methods. More specifically, we consider a Bayesian analysis for the variables of interest (i.e., the parameters of the model to invert), whereas we employ a maximum likelihood approach for the estimation of the noise power. The whole technique is implemented by means of an iterative procedure with alternating sampling and optimization steps. Moreover, the noise power is also used as a tempered parameter for the posterior distribution of the the variables of interest. Therefore, a sequence of tempered posterior densities is generated, where the tempered parameter is automatically selected according to the current estimate of the noise power. A complete Bayesian study over the model parameters and the scale parameter can also be performed. Numerical experiments show the b...
The Astronomical Journal, 2020
The Astronomical Journal, 2018
Astronomy & Astrophysics, 2017
Astronomy & Astrophysics, 2016
Monthly Notices of the Royal Astronomical Society, 2015
Astronomy & Astrophysics, 2014
Astrophysics and Space Science, 2014
EAS Publications Series, 2005
Highlights of Spanish Astrophysics III, 2003
... HIGH TEMPORAL RESOLUTION OF THE FLARE STAR AD Leo I. CRESPO-CHACON1, D. MONTES1, J. LOPEZ-SAN... more ... HIGH TEMPORAL RESOLUTION OF THE FLARE STAR AD Leo I. CRESPO-CHACON1, D. MONTES1, J. LOPEZ-SANTIAGO1 MJ FERNANDEZ-FIGUEROA1, D ... galaxies up to redshifts zā 2. 5 for subsequent optical and NIR spectroscopic studies with EMIR and OSIRIS on the ...
2020 28th European Signal Processing Conference (EUSIPCO), 2021
Monthly Notices of the Royal Astronomical Society, 2021
Model fitting is possibly the most extended problem in science. Classical approaches include the ... more Model fitting is possibly the most extended problem in science. Classical approaches include the use of least-squares fitting procedures and maximum likelihood methods to estimate the value of the parameters in the model. However, in recent years, Bayesian inference tools have gained traction. Usually, Markov chain Monte Carlo (MCMC) methods are applied to inference problems, but they present some disadvantages, particularly when comparing different models fitted to the same data set. Other Bayesian methods can deal with this issue in a natural and effective way. We have implemented an importance sampling (IS) algorithm adapted to Bayesian inference problems in which the power of the noise in the observations is not known a priori. The main advantage of IS is that the model evidence can be derived directly from the so-called importance weights ā while MCMC methods demand considerable postprocessing. The use of our adaptive target adaptive importance sampling (ATAIS) method is shown ...
Mathematics, 2021
We propose a novel adaptive importance sampling scheme for Bayesian inversion problems where the ... more We propose a novel adaptive importance sampling scheme for Bayesian inversion problems where the inference of the variables of interest and the power of the data noise are carried out using distinct (but interacting) methods. More specifically, we consider a Bayesian analysis for the variables of interest (i.e., the parameters of the model to invert), whereas we employ a maximum likelihood approach for the estimation of the noise power. The whole technique is implemented by means of an iterative procedure with alternating sampling and optimization steps. Moreover, the noise power is also used as a tempered parameter for the posterior distribution of the the variables of interest. Therefore, a sequence of tempered posterior densities is generated, where the tempered parameter is automatically selected according to the current estimate of the noise power. A complete Bayesian study over the model parameters and the scale parameter can also be performed. Numerical experiments show the b...
The Astronomical Journal, 2020
The Astronomical Journal, 2018
Astronomy & Astrophysics, 2017
Astronomy & Astrophysics, 2016
Monthly Notices of the Royal Astronomical Society, 2015
Astronomy & Astrophysics, 2014
Astrophysics and Space Science, 2014
EAS Publications Series, 2005
Highlights of Spanish Astrophysics III, 2003
... HIGH TEMPORAL RESOLUTION OF THE FLARE STAR AD Leo I. CRESPO-CHACON1, D. MONTES1, J. LOPEZ-SAN... more ... HIGH TEMPORAL RESOLUTION OF THE FLARE STAR AD Leo I. CRESPO-CHACON1, D. MONTES1, J. LOPEZ-SANTIAGO1 MJ FERNANDEZ-FIGUEROA1, D ... galaxies up to redshifts zā 2. 5 for subsequent optical and NIR spectroscopic studies with EMIR and OSIRIS on the ...