Results 131 to 140 of about 47,776 (252)

Modelling Skin Pigmentation Using the Monte Carlo Technique: A Review. [PDF]

Sensors (Basel)
Al-Halawani R, Qassem M, Kyriacou PA.
europepmc   +1 more source

Stratified Monte Carlo Methods for the simulation of Markov chains

, 2016
Les méthodes de Monte Carlo sont des méthodes probabilistes qui utilisent des ordinateurs pour résoudre de nombreux problèmes de la science à l’aide de nombres aléatoires. Leur principal inconvénient est leur convergence lente.
El maalouf, Joseph
core  

Forecasting Count Data With Varying Dispersion: A Latent‐Variable Approach

Journal of Forecasting, Volume 45, Issue 4, Page 1985-2000, July 2026.
ABSTRACT Count data, such as product sales and disease case counts, are common in business forecasting and many areas of science. Although the Poisson distribution is the best known model for such data, its use is severely limited by its assumption that the dispersion is a fixed function of the mean, which rarely holds in real‐world scenarios.
Easton Huch, Candace Berrett, Mason Ferlic, Kimberly F. Sellers   +3 more
wiley   +1 more source

Federated Semi-Supervised Learning with Uniform Random and Lattice-Based Client Sampling. [PDF]

Entropy (Basel)
Zhang M, Yang F.
europepmc   +1 more source

Generation of Random Numbers and Parallel Random Number Streams for Monte Carlo Simulations

, 2012
Modern methods and libraries for high quality pseudorandom number generation and for generation of parallel random number streams for Monte Carlo simulations are considered.
L. N. Shchur, L. Yu. Barash
core  

A Comparative Study of IVIM‐MRI Fitting Techniques in Glioma Grading: Conventional, Bayesian, and Voxel‐Wise and Spatially‐Aware Deep Learning Approaches

Journal of Magnetic Resonance Imaging, Volume 64, Issue 1, Page 184-200, July 2026.
ABSTRACT Background Intravoxel incoherent motion (IVIM) analysis of diffusion‐weighted MRI (DWI) provides microvascular perfusion and diffusion information. However, parameter estimation is limited by noise sensitivity, variability across fitting methods, and lack of standardization.
Misha P. T. Kaandorp, Andras Jakab, Christian Federau, Peter T. While   +3 more
wiley   +1 more source

Low‐normal birthweight in the first pregnancy as a risk factor for small for gestational age in the second pregnancy: A matched case–control study

Pregnancy, Volume 2, Issue 4, July 2026.
Abstract Background Recurrent obstetric complications may stem from chronic placental dysfunction or maternal vulnerability, with potential worsening across successive pregnancies. Subtle intrauterine growth restriction in a first pregnancy, even within the appropriate‐for‐gestational‐age (AGA) range, may signal underlying risk for subsequent adverse ...
Boujenah Jeremy, Beddock Richard, Patrick Rozenberg   +2 more
wiley   +1 more source

Caveats on Using Firth's Penalization in the Model‐Based Regression Standardization for Rare Diseases

Statistics in Medicine, Volume 45, Issue 15-17, July 2026.
ABSTRACT Model‐based regression standardization, also known as the parametric g‐formula, is widely used to estimate marginal effect measures. However, in rare disease settings, the small number of observed events relative to the number of covariates can lead to (quasi‐)complete separation, resulting in non‐convergent estimates in the regression models.
Sotaro Hashibe, Wataru Hongo, Tomohiro Shinozaki   +2 more
wiley   +1 more source

Estimation of (co)variance components for very large datasets and complex single-step genomic models. [PDF]

Genet Sel Evol
Bermann M, Legarra A, Aguilar I, Alvarez-Munera A, Misztal I, Lourenco D.   +5 more
europepmc   +1 more source

Variance Reduction Techniques in Monte Carlo Methods

Monte Carlo methods are simulation algorithms to estimate a numerical quantity in a statistical model of a real system. These algorithms are executed by computer programs.
Ridder, A.A.N., Kleijnen, Jack P.C., Rubinstein, R.Y.   +2 more
core