J. López‐Santiago

Unsupervised sifting of XMM–Newton EPIC observations using variational autoencoders

Manuel A. Vázquez, Jesus Cid-Sueiro, J. López‐Santiago

ABSTRACT Finding specific events of interest within the XMM–Newton Science Archive (XSA) is a significant challenge due to the sheer volume of data, which contains over 300 000 source light curves. While the mission provides standardized multiband products, the vast majority of …

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Design of risk measures for ship collision avoidance using radar observations and Bayesian filtering

Ernesto Curbelo, Luca Martino, J. López‐Santiago

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A recurrent 70–100 min quasi-periodic pulsation in the intermediate-aged mid-M dwarf GJ 3512

J. López‐Santiago, F. Reale, G Micela, Luca Martino et autres

ABSTRACT We report the discovery of a recurrent quasi-periodic pulsation (QPP) in the late-M dwarf GJ 3512 (M5.5V) using multiple Transiting Exoplanet Survey Satellite (TESS) data sets. A strong signal with a period of 70–100 min was detected in wavelet analyses of …

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Data-driven informative priors for Bayesian inference with quasi-periodic data

J. López‐Santiago, Luca Martino, Joaquı́n Mı́guez, Gonzalo Vazquez-Vilar

Bayesian computational strategies for inference can be inefficient in approximating the posterior distribution in models that exhibit some form of periodicity. This is because the probability mass of the marginal posterior distribution of the parameter representing the period is usually highly concentrated …

Maximum likelihood inference for a class of discrete-time Markov switching time series models with multiple delays

José. A. Martínez-Ordoñez, J. López‐Santiago, Joaquı́n Mı́guez

Abstract Autoregressive Markov switching (ARMS) time series models are used to represent real-world signals whose dynamics may change over time. They have found application in many areas of the natural and social sciences, as well as in engineering. In general, inference in …

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Maximum likelihood inference for a class of discrete-time Markov-switching time series models with multiple delays

José A. Martínez-Ordóñez, J. López‐Santiago, Joaquı́n Mı́guez

Autoregressive Markov switching (ARMS) time series models are used to represent real-world signals whose dynamics may change over time. They have found application in many areas of the natural and social sciences, as well as in engineering. In general, inference in this …

Marginal Likelihood Computation for Model Selection and Hypothesis Testing: An Extensive Review

Fernando Llorente, Luca Martino, David Delgado‐Gómez, J. López‐Santiago

This is an up-to-date introduction to, and overview of, marginal likelihood computation for model selection and hypothesis testing. Computing normalizing constants of probability models (or ratios of constants) is a fundamental issue in many applications in statistics, applied mathematics, signal processing, and …

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On the safe use of prior densities for Bayesian model selection

Fernando Llorente, Luca Martino, Ernesto Curbelo, J. López‐Santiago et autres

Abstract The application of Bayesian inference for the purpose of model selection is very popular nowadays. In this framework, models are compared through their marginal likelihoods, or their quotients, called Bayes factors. However, marginal likelihoods depend on the prior choice. For model …

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X-ray variability of HD 189733 across eight years of XMM-Newton observations

I. Pillitteri, G. Micela, A. Maggio, S. Sciortino et autres

The characterization of exoplanets, their formation, evolution, and chemical changes is tightly linked to our knowledge of their host stars. In particular, stellar X-rays and UV emission have a strong impact on the dynamical and chemical evolution of planetary atmospheres. We analyzed …

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A Bayesian inference and model selection algorithm with an optimization scheme to infer the model noise power

J. López‐Santiago, Luca Martino, Manuel A. Vázquez, Joaquı́n Mı́guez

ABSTRACT 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 …

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Automatic Tempered Posterior Distributions for Bayesian Inversion Problems

Luca Martino, Fernando Llorente, Ernesto Curbelo, J. López‐Santiago et autres

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 …

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Uncertainty Propagation Meeting Space Debris Needs

Benjamin Bastida Virgili, Jorge Bravo Aguado, Alejandro Cano, Diego A. Escobar et autres