Results 71 to 80 of about 66,786 (305)
Advanced Science, EarlyView.A gap‐free genome assembly and multi‐omics comparison of the terrestrial slug Laevichaulis alte with an aquatic relative reveal that expansion of the VEGF family orchestrates mucus production, lipid metabolism, and immune defense—highlighting key molecular innovations for conquering life on land.
Gang Wang +19 more
wiley +1 more source
Markov Chain Monte Carlo Based Energy Use Behaviors Prediction of Office Occupants
Algorithms, 2020 Prediction of energy use behaviors is a necessary prerequisite for designing personalized and scalable energy efficiency programs. The energy use behaviors of office occupants are different from those of residential occupants and have not yet been ...
Qiao Yan +4 more
doaj +1 more source
Markov chain Monte Carlo methods for state-space models with point process observations [PDF]
, 2012 This letter considers how a number of modern Markov chain Monte Carlo (MCMC) methods can be applied for parameter estimation and inference in state-space models with point process observations.
Niranjan, Mahesan +2 more
core +1 more source
The Geography of Success: A Spatial Analysis of Export Intensity in the Italian Wine Industry
Agribusiness, EarlyView.ABSTRACT This paper investigates the paradox of how Italy's fragmented, SME‐dominated wine industry achieves global export success. Moving beyond purely firm‐centric explanations, we test whether export intensity is spatially dependent, clustering geographically in regional ecosystems.
Nicolas Depetris Chauvin, Jonas Di Vita
wiley +1 more source
Alternatives To The MCMC Method [PDF]
AIP Conference Proceedings, 2004 The Markov Chain Monte Carlo method (MCMC) is often used to generate independent (pseudo) random numbers from a distribution with a density that is known only up to a normalising constant. With the MCMC method it is not necessary to compute the normalising constant (see e.g. Tierney, 1994; Besag, 2000).
openaire +2 more sources
Accelerating equilibrium spin-glass simulations using quantum data and deep learning
, 2022 Release linked to Zenodo. Relevant features: Autoregressive neural networks (MADE, PixelCNN); Monte Carlo methods (SSF-MCMC, N-MCMC, H-MCMC).If you use this software, please cite it as ...
Scriva, Giuseppe
core +1 more source
Advanced Intelligent Discovery, EarlyView.Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin +4 more
wiley +1 more source
International Journal of Assessment Tools in EducationThis research aims to compare the ability and item parameter estimations of Item Response Theory according to Maximum likelihood and Bayesian approaches in different Monte Carlo simulation conditions.
Ergül Demir, Eray Selçuk
doaj +1 more source
MCMC-estimated parameter values for the infection MMs*.
, 2019 MCMC-estimated parameter values for the infection MMs*.
Catherine A. A. Beauchemin (236713) +4 more
core +1 more source