This paper considers likelihood-based inference for the family of power distributions. Widely applicable results are presented which can be used to conduct inference for all three parameters of the general location-scale ...

Cosmic inflation may have led to non-Gaussian initial conditions that cannot be fully parametrised by 3- and/or 4-point functions. In this work, we discuss various strategies to search for primordial non-Gaussianity beyond ...

We consider random generalizations of a quantum model of infinite range introduced by Emch and Radin. The generalizations allow a neat extension from the class l (1) of absolutely summable lattice potentials to the optimal ...

The uncertainty is intrinsic in machine learning since it is present in data, models, parameters, and prediction. The Bayesian approach to machine learning considers all the uncertainty under the same point of view, and ...

This thesis deals with Bayesian methods for solving ill-posed inverse problems in imaging with learnt image priors. The first part of this thesis (Chapter 3) concentrates on two particular problems, namely joint denoising ...

El Síndrome respiratorio agudo grave, es una patología ocasionada por el COVID-19 que con el paso de los meses ha generado el aumento de mortalidad y morbilidad en nuestro país, muchas de estas personas están hospitalizadas ...

La distribución Unitaria Birnbaum Saunders (UBS), [Mazucheli et al., 2018a], tiene soporte en el intervalo (0,1), motivo por el cual se emplea con éxito en el modelamiento de tasas e indicadores. Se presentan dos nuevas ...

Los pares de fotones entrelazados obtenidos con el fenómeno de la Conversión Paramétrica Espontánea Descendente (SPDC, Spontaneous Parametric Down Conversion) se encuentran en un estado compuesto denominado bifotón, de ...

A conceptual issue regarding gene regulatory network (GRN) inference algorithms is establishing their validity or correctness. In this study, we argue that for this purpose it is useful to conceive these algorithms as ...