相关论文: The Bjorken sum rule with Monte Carlo and Neural N…

We present a Monte Carlo based analysis of the combined world data on polarized lepton-nucleon deep-inelastic scattering at small Bjorken $x$ within the polarized quark dipole formalism. We show for the first time that double-spin…

We calculate and analize the ${\cal{O}}(\alpha_s)$ one-particle inclusive cross section in polarized deep inelastic lepton-hadron scattering, using dimensional regularization and the HVBM prescription for $\gamma_5$. We discuss the…

Polarized deep inelastic scattering (DIS) data are analyzed in leading and next-to-leading order of QCD within the common `standard' scenario of polarized parton distributions with a flavor-symmetric light sea (antiquark) distribution…

Diffusion models (DMs) have recently shown outstanding capabilities in modeling complex image distributions, making them expressive image priors for solving Bayesian inverse problems. However, most existing DM-based methods rely on…

Normal factor graph duality offers new possibilities for Monte Carlo algorithms in graphical models. Specifically, we consider the problem of estimating the partition function of the ferromagnetic Ising and Potts models by Monte Carlo…

We introduce a new approach for amortizing inference in directed graphical models by learning heuristic approximations to stochastic inverses, designed specifically for use as proposal distributions in sequential Monte Carlo methods. We…

A probabilistic approach to phase-field brittle and ductile fracture with random material and geometric properties is proposed within this work. In the macroscopic failure mechanics, materials properties and exactness of spatial quantities…

We analyse a multilevel Monte Carlo method for the approximation of distribution functions of univariate random variables. Since, by assumption, the target distribution is not known explicitly, approximations have to be used. We provide an…

The Pauli exclusion principle is advocated for constructing the proton and neutron deep inelastic structure functions in terms of Fermi-Dirac distributions that we parametrize with very few parameters. It allows a fair description of the…

We present the results of our QCD analysis for polarized quark distribution and structure function $xg_1 (x,Q^2)$. We use very recently experimental data to parameterize our model. New parameterizations are derived for the quark and gluon…

We present a next-to-leading order (NLO) computation of the full set of polarized and unpolarized electroweak semi-inclusive DIS (SIDIS) structure functions, whose knowledge is crucial for a precise extraction of polarized parton…

We perform a new extraction of polarized parton distribution functions (PPDFs) from the spin structure function experimental data in the fixed-flavor number scheme (FFNS). In this analysis, we include recent proton and deuteron spin…

We present a NLO perturbative analysis of all available data on the polarized structure function g_1(x,Q^2) with the aim of making a quantitative test of the validity of the Bjorken sum rule, of measuring \alpha_s, and of deriving helicity…

We present a comprehensive new global QCD analysis of unpolarized parton distribution functions (PDFs) based upon proton, deuteron and $A\!=\!3$ data, including the latest inclusive deep-inelastic scattering (DIS) measurements from…

We present a new Markov chain Monte Carlo method for estimating posterior probabilities of structural features in Bayesian networks. The method draws samples from the posterior distribution of partial orders on the nodes; for each sampled…

We present a new method to extract parton distribution functions from high energy experimental data based on a specific type of neural networks, the Self-Organizing Maps. We illustrate the features of our new procedure that are particularly…

The $\rho$ meson polarized generalized parton distribution functions, its structure functions $g_1$ and $g_2$ and its axial form factors ${\tilde G}_{1,2}$ are studied based on a light-front quark model for the first time. Comparing our…

We present recent results of the NNPDF collaboration on a full DIS analysis of Parton Distribution Functions (PDFs). Our method is based on the idea of combining a Monte Carlo sampling of the probability measure in the space of PDFs with…

Motivated by applications to Bayesian inference for statistical models with orthogonal matrix parameters, we present $\textit{polar expansion},$ a general approach to Monte Carlo simulation from probability distributions on the Stiefel…

Monte Carlo (MC) algorithms are commonly employed to explore high-dimensional parameter spaces constrained by data. All the statistical information obtained in the output of these analyses is contained in the Markov chains, which one needs…