Results 31 to 40 of about 44,986 (305)

Measuring Stellar Radial Velocity using Markov Chain Monte Carlo(MCMC) Method [PDF]

Proceedings of the International Astronomical Union, 2013
AbstractStellar radial velocity is estimated by using template fitting and Markov Chain Monte Carlo(MCMC) methods. This method works on the LAMOST stellar spectra. The MCMC simulation generates a probability distribution of the RV. The RV error can also computed from distribution.
Yihan Song, Ali Luo, Yongheng Zhao
openaire   +1 more source

WinBUGS for population ecologists: Bayesian modeling using Markov Chain Monte Carlo methods. [PDF]

, 2008
This paper was presented at the EURING 2007 Technical Meeting, January 14-21, Dunedin, New Zealand. It has been submitted for publication in the conference proceedings, which will appear as a special issue of Environmental and Ecological Statistics.The ...
Grosbois, V, Richard A. Parker, Jamieson, Lara E., Grosbois, Vladimir, Brooks, Stephen P., Giminez, O, Bonner, Simon J., Parker, R A, Morgan, Byron J., Olivier Gimenez, Simon J. Bonner, Morgan, Byron J. T., Parker, Richard A., Lara E. Jamieson, Ruth King, Jamieson, L E, Morgan, B J T, Jamieson, L.E., Gimenez, Olivier, Byron J.T. Morgan, Len Thomas, Vladimir Grosbois, King, Ruth, Brooks, S P, Stephen P. Brooks, Bonner, S J, Thomas, Len   +26 more
core   +1 more source

Speculative moves : multithreading Markov Chain Monte Carlo programs [PDF]

, 2008
The increasing availability of multi-core and multi-processor architectures provides new opportunities for improving the performance of many computer simulations.
Bhalerao, Abhir, Byrd, Jonathan M. R., Jarvis, Stephen A.   +2 more
core  

The random walk Metropolis : linking theory and practice through a case study. [PDF]

, 2009
The random walk Metropolis (RWM) is one of the most common Markov Chain Monte Carlo algorithms in practical use today. Its theoretical properties have been extensively explored for certain classes of target, and a number of results with important ...
Fearnhead, Paul, Chris Sherlock, Sherlock, Chris, Paul Fearnhead, Roberts, Gareth, Roberts, Gareth O., Gareth O. Roberts   +6 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

pyLAIS: A Python package for Layered Adaptive Importance Sampling

SoftwareX
Monte Carlo (MC) techniques are widely used to draw from complex distributions and/or for the calculation of related integrals. The most famous families of MC methods are Markov Chain Monte Carlo (MCMC) and importance sampling (IS).
Ernesto Curbelo, Luca Martino, David Delgado-Gómez   +2 more
doaj   +1 more source

Minimizing transmission loss using inspired ant colony optimization and Markov Chain Monte Carlo in underwater WSN environment

Journal of Ocean Engineering and Science, 2019
In Underwater Wireless Sensor Networks (UWSNs), the most important challenging issues are propagation delay, high error probability, high latency, high communication cost, limited bandwidth, limited memory, low packet delivery ratio, and transmission ...
Raj Priyadarshini R, Sivakumar N
doaj   +1 more source

spNNGP R Package for Nearest Neighbor Gaussian Process Models

Journal of Statistical Software, 2022
This paper describes and illustrates functionality of the spNNGP R package. The package provides a suite of spatial regression models for Gaussian and non-Gaussian pointreferenced outcomes that are spatially indexed. The package implements several Markov
Andrew O. Finley, Abhirup Datta, Sudipto Banerjee   +2 more
doaj   +1 more source

On the containment condition for adaptive Markov Chain Monte Carlo algorithms [PDF]

, 2009
This paper considers ergodicity properties of certain adaptive Markov chain Monte Carlo (MCMC) algorithms for multidimensional target distributions, in particular Adaptive Metropolis and Adaptive Metropolis-within-Gibbs.
Rosenthal, Jeffrey S. (Jeffrey Seth), Bai, Yan, Roberts, Gareth O.   +2 more
core  

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

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, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source