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Uncertainty of decisions in safety-critical engineering applications can be estimated on the basis of the Bayesian Markov Chain Monte Carlo (MCMC) technique of averaging over decision models. The use of decision tree (DT) models assists…
Recent cosmic-ray measurements are challenging our models of propagation in the Galaxy. A good characterization of the secondary cosmic rays (B, Be, Li and sub-iron species) is crucial to constrain these models and exploit the precision of…
We have performed an overall fit to the electroweak data with the generation blind $U(1)$ extension of the Standard Model. As input data for fitting we have included the asymmetry parameters, the particle decay widths of $Z$, neutrino…
We present new atomic data (radiative transitions rates and collision strengths) from large scale calculations and a non-LTE spectral model for Fe III. This model is in very good agreement with observed astronomical emission spectra, in…
Electron collisions with O$_2$ at scattering energies below 1 eV are studied in the fixed-nuclei approximation for a range of internuclear separations using the ab initio molecular R-matrix method. The $^2\Pi_g$ scattering eigenphases and…
We present an extension of the Levenberg-Marquardt algorithm for fitting multichannel nuclear cross section data. Our approach offers a practical and robust alternative to conventional trust-region methods for analyzing experimental data.…
The information-geometric statistical analysis on the stability of model reductions, reported previously [Imbri\v{s}ak and Nomura, Phys. Rev. C 107, 034304 (2023)] with a focus on the manifold boundary approximation method in the…
We introduce a Bayesian protocol based on artificial neural networks that is suitable for modeling inclusive electron-nucleus scattering on a variety of nuclear targets with quantified uncertainties. Unlike previous applications in the…
Due to their ability to recognize complex patterns, neural networks can drive a paradigm shift in the analysis of materials science data. Here, we introduce ARISE, a crystal-structure identification method based on Bayesian deep learning.…
We present a Bayesian approach to estimate the parameters of mathematical models of cardiac electrophysiology with quantified uncertainty. Such models capture the dynamics of the electrical signal that coordinates the muscle cell…
Nuclear Magnetic Resonance (NMR) spectra are widely used in metabolomics to obtain profiles of metabolites dissolved in biofluids such as cell supernatants. Methods for estimating metabolite concentrations from these spectra are presently…
We present an introduction to some concepts of Bayesian data analysis in the context of atomic physics. Starting from basic rules of probability, we present the Bayes' theorem and its applications. In particular we discuss about how to…
We consider three recent large-scale calculations for the radiative and electron-impact excitation data of N IV, carried out with different methods and codes. The scattering calculations employed the relativistic Dirac $R$-matrix (DARC)…
We make ab initio predictions for the A = 6 nuclear level scheme based on two- and three-nucleon interactions up to next-to-next-to-leading order in chiral effective field theory ($\chi$EFT). We utilize eigenvector continuation and Bayesian…
AMS-02 on the International Space Station has been releasing data of unprecedented accuracy. This poses new challenges for their interpretation. We refine the methodology to get a statistically sound determination of the cosmic-ray…
The different facets of the $R$-matrix method are presented pedagogically in a general framework. Two variants have been developed over the years: $(i)$ The "calculable" $R$-matrix method is a calculational tool to derive scattering…
Bayesian network (BN) modelling is extensively used in systems epidemiology. Usually it consists in selecting and reporting the best-fitting structure conditional to the data. A major practical concern is avoiding overfitting, on account of…
We introduce a hierarchical framework that combines ab initio many-body calculations with a Bayesian neural network, developing emulators capable of accurately predicting nuclear properties across isotopic chains simultaneously and being…
Statistical tools of uncertainty quantification can be used to assess the information content of measured observables with respect to present-day theoretical models; to estimate model errors and thereby improve predictive capability; to…
Monte Carlo simulations are widely used in nuclear physics to model experimental systems. In cases where there are significant unknown quantities, such as energies of states, an iterative process of simulating and fitting is often required…