Results 51 to 60 of about 221,738 (314)
dalmatian: A Package for Fitting Double Hierarchical Linear Models in R via JAGS and nimble
Journal of Statistical Software, 2021 Traditional regression models, including generalized linear mixed models, focus on understanding the deterministic factors that affect the mean of a response variable.
Simon Bonner +5 more
doaj +1 more source
Business Strategy and the Environment, EarlyView.ABSTRACT This article contributes to sustainability research by investigating the complex, geopolitically induced challenges faced by industrial supply chains under international sanctions. Using Iran's steel industry as a case, it examines sustainability barriers through the lens of stakeholder theory. A mixed methods approach was employed.
Seyed Hamed Moosavirad +2 more
wiley +1 more source
Consistency of Markov chain quasi-Monte Carlo on continuous state spaces
, 2009 The random numbers driving Markov chain Monte Carlo (MCMC) simulation are usually modeled as independent U(0,1) random variables. Tribble [Markov chain Monte Carlo algorithms using completely uniformly distributed driving sequences (2007) Stanford Univ.]
Chen, S., Dick, J., Owen, A. B.
core +5 more sources
Restricted Tweedie stochastic block models
Canadian Journal of Statistics, EarlyView.Abstract The stochastic block model (SBM) is a widely used framework for community detection in networks, where the network structure is typically represented by an adjacency matrix. However, conventional SBMs are not directly applicable to an adjacency matrix that consists of nonnegative zero‐inflated continuous edge weights.
Jie Jian, Mu Zhu, Peijun Sang
wiley +1 more source
Distance between configurations in Markov chain Monte Carlo simulations
Journal of High Energy Physics, 2017 For a given Markov chain Monte Carlo algorithm we introduce a distance between two configurations that quantifies the difficulty of transition from one configuration to the other configuration.
Masafumi Fukuma +2 more
doaj +1 more source
A goodness‐of‐fit test for regression models with discrete outcomes
Canadian Journal of Statistics, EarlyView.Abstract Regression models are often used to analyze discrete outcomes, but classical goodness‐of‐fit tests such as those based on the deviance or Pearson's statistic can be misleading or have little power in this context. To address this issue, we propose a new test, inspired by the work of Czado et al.
Lu Yang +2 more
wiley +1 more source
MCMCpack: Markov Chain Monte Carlo in R
Journal of Statistical Software, 2011 We introduce MCMCpack, an R package that contains functions to perform Bayesian inference using posterior simulation for a number of statistical models. In addition to code that can be used to fit commonly used models, MCMCpack also contains some useful ...
Andrew D. Martin +2 more
doaj
A Hamiltonian Monte Carlo method for Bayesian Inference of Supermassive Black Hole Binaries
, 2013 We investigate the use of a Hamiltonian Monte Carlo to map out the posterior density function for supermassive black hole binaries. While previous Markov Chain Monte Carlo (MCMC) methods, such as Metropolis-Hastings MCMC, have been successfully employed ...
Carré, Jérôme, Porter, Edward K.
core +3 more sources
Contributions to Plasma Physics, EarlyView.ABSTRACT Ab initio path integral Monte Carlo (PIMC) simulations constitute the gold standard for the estimation of a broad range of equilibrium properties of a host of interacting quantum many‐body systems spanning a broad range of conditions from ultracold atoms to warm dense quantum plasmas.
Paul Hamann +2 more
wiley +1 more source
Ecography, EarlyView.The spatial ecology of stalk‐and‐ambush predators like the Eurasian lynx Lynx lynx depends on prey availability and environmental features, yet the relative roles of these factors remain unclear at large spatial scales. In this study, we analysed lynx habitat use across central and southern Finland using snow‐track data from the Wildlife Triangle ...
Francesca Malcangi +4 more
wiley +1 more source