Zobrazeno 1 - 10
of 2 647
pro vyhledávání: '"Martino, L."'
Publikováno v:
Expert Systems with Applications, Volume 231, 30 November 2023, 120705
We introduce a generalized information criterion that contains other well-known information criteria, such as Bayesian information Criterion (BIC) and Akaike information criterion (AIC), as special cases. Furthermore, the proposed spectral informatio
Externí odkaz:
http://arxiv.org/abs/2308.09108
Publikováno v:
Digital Signal Processing, Volume 140, 2023, 104103
We design a Universal Automatic Elbow Detector (UAED) for deciding the effective number of components in model selection problems. The relationship with the information criteria widely employed in the literature is also discussed. The proposed UAED d
Externí odkaz:
http://arxiv.org/abs/2308.09102
Autor:
Janna van Wetering, Hanne Geut, John J. Bol, Yvon Galis, Evelien Timmermans, Jos W.R. Twisk, Dagmar H. Hepp, Martino L. Morella, Lasse Pihlstrom, Afina W. Lemstra, Annemieke J.M. Rozemuller, Laura E. Jonkman, Wilma D.J. van de Berg
Publikováno v:
Acta Neuropathologica Communications, Vol 12, Iss 1, Pp 1-17 (2024)
Abstract Background Neuroinflammation and Alzheimer’s disease (AD) co-pathology may contribute to disease progression and severity in dementia with Lewy bodies (DLB). This study aims to clarify whether a different pattern of neuroinflammation, such
Externí odkaz:
https://doaj.org/article/e051b3b491424f4080696dca27f49c22
Publikováno v:
IEEE-ACM Transactions on Audio, Speech and Language Processing, 2022
In the last decade, soundscapes have become one of the most active topics in Acoustics, providing a holistic approach to the acoustic environment, which involves human perception and context. Soundscapes-elicited emotions are central and substantiall
Externí odkaz:
http://arxiv.org/abs/2207.12743
Publikováno v:
WIREs Computational Statistics, 2022
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 depe
Externí odkaz:
http://arxiv.org/abs/2206.05210
Publikováno v:
Monthly Notices of the Royal Astronomical Society, Volume 507, Issue 3, Pages 3351-3361, 2021
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, Ba
Externí odkaz:
http://arxiv.org/abs/2108.02894
Publikováno v:
International Statistical Review. 2024
This survey gives an overview of Monte Carlo methodologies using surrogate models, for dealing with densities which are intractable, costly, and/or noisy. This type of problem can be found in numerous real-world scenarios, including stochastic optimi
Externí odkaz:
http://arxiv.org/abs/2108.00490
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Volume 2020, Article number: 25 (2020)
Statistical signal processing applications usually require the estimation of some parameters of interest given a set of observed data. These estimates are typically obtained either by solving a multi-variate optimization problem, as in the maximum li
Externí odkaz:
http://arxiv.org/abs/2107.11820
Publikováno v:
Mathematics. 2021; 9(7):784
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 is split. More specifically, we consider a Bayesian analysis for the variables of
Externí odkaz:
http://arxiv.org/abs/2107.11614
Publikováno v:
Applied Mathematical Modelling, Volume 11, Pages 310-331, 2022
Monte Carlo sampling methods are the standard procedure for approximating complicated integrals of multidimensional posterior distributions in Bayesian inference. In this work, we focus on the class of Layered Adaptive Importance Sampling (LAIS) sche
Externí odkaz:
http://arxiv.org/abs/2105.02579