Results 61 to 70 of about 44,758 (308)
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) +2 more
core
Industry Portfolio Volatility Connections and Industry Portfolio Returns
International Journal of Finance &Economics, EarlyView.ABSTRACT This paper tracks dynamic connections that form among daily US industry portfolio return volatilities using a Bayesian time‐varying parameter VAR model. Market participants often focus on sectors to filter vast amounts of information, and this focus results in cross‐industry return predictability. We characterise connections that form over the
Michael Ellington +2 more
wiley +1 more source
Evaluation of fall‐seeded cover crops for grassland nesting waterfowl in eastern South Dakota
Wildlife Society Bulletin, EarlyView., 2023 Cover crops are experiencing a revival among Midwestern farmers, and we assessed their attractiveness and safety for nesting ducks in South Dakota. Nest success was markedly lower in cover crops than in perennial cover during both years of our study, including 2019 which was a best‐case scenario for cover crops, with extremely wet conditions delaying ...
Charles W. Gallman +3 more
wiley +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 +2 more
core
Efficient Markov chain Monte Carlo sampling for electrical impedance tomography
Computer Assisted Methods in Engineering and Science, 2017 This paper studies electrical impedance tomography (EIT) using Bayesian inference [1]. The resulting posterior distribution is sampled by Markov chain Monte Carlo (MCMC) [2]. This paper studies a toy model of EIT as the one presented in [3], and focuses
Erfang Ma
doaj +1 more source
International Journal of Gynecology &Obstetrics, EarlyView.Abstract Objective This study identifies factors associated with not proceeding to clitoral reconstructive surgery among women with female genital mutilation (FGM) enrolled in a specialized surgical pathway. Methods A retrospective cohort study was conducted at a multidisciplinary referral center in Montreuil, France, between January 2021 and December ...
Félicia Joinau‐Zoulovits +3 more
wiley +1 more source
Application of Markov chain Monte carlo method in Bayesian statistics
MATEC Web of Conferences, 2016 In statistical inference methods, bayesian method is a method of great influence. This paper introduces the basic idea of the bayesian method. However, the widespread popularity of MCMC samplers is largely due to their impact on solving statistical ...
Zhao Qi
doaj +1 more source
Rare vasculitis types and obstetric and neonatal outcomes – A population‐based study
International Journal of Gynecology &Obstetrics, EarlyView.Abstract Objective Vasculitis is an infrequent pathology among reproductive‐aged women. While data exists regarding pregnancy outcomes in the more common vasculitis subtypes, data is limited regarding these outcomes in rare vasculitis subtypes. We aimed to compare pregnancy and perinatal outcomes between women who suffered from rare types of vasculitis
Uri Amikam +4 more
wiley +1 more source
Diffusion limits of the random walk Metropolis algorithm in high dimensions [PDF]
, 2012 Diffusion limits of MCMC methods in high dimensions provide a useful theoretical tool for studying computational complexity. In particular, they lead directly to precise estimates of the number of steps required to explore the target measure, in ...
Stuart, Andrew M. +9 more
core +1 more source
Advanced MCMC methods for sampling on diffusion pathspace
Stochastic Processes and their Applications, 2013 The need to calibrate increasingly complex statistical models requires a persistent effort for further advances on available, computationally intensive Monte Carlo methods. We study here an advanced version of familiar Markov Chain Monte Carlo (MCMC) algorithms that sample from target distributions defined as change of measures from Gaussian laws on ...
Alexandros Beskos +2 more
openaire +4 more sources