Results 91 to 100 of about 85,479 (297)
Bayesian Adaptive Hamiltonian Monte Carlo with an Application to High-Dimensional BEKK GARCH Models [PDF]
Hamiltonian Monte Carlo (HMC) is a recent statistical procedure to sample from complex distributions. Distant proposal draws are taken in a equence of steps following the Hamiltonian dynamics of the underlying parameter space, often yielding superior ...
John Maheu, Martin Burda
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Journal of Adolescence, EarlyView.ABSTRACT Introduction There is a growing interest in positive risk‐taking (PRT) during adolescence and young adulthood. Emerging evidence has documented positive associations of PRT with multiple positive adolescent socioemotional developmental outcomes, including prosocial behavior.
Weiyu Edith Chen, Hao Zheng, Yao Zheng
wiley +1 more source
Zero variance in Markov chain Monte Carlo with an application to credit risk estimation [PDF]
We propose a general purpose variance reduction technique for Markov Chain Monte Carlo estimators based on the Zero-Variance principle introduced in the physics lit- erature by Assaraf and Caarel ( 1999). The potential of the new idea is illustrated with
Tenconi Paolo
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Parallel hierarchical sampling : a general-purpose class of multiple-chains MCMC algorithms [PDF]
, 2009 This paper introduces the Parallel Hierarchical Sampler (PHS), a class of Markov chain Monte Carlo algorithms using several interacting chains having the same target distribution but different mixing properties. Unlike any single-chain MCMC algorithm,
Mira, Antonietta +3 more
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JCPP Advances, EarlyView.Abstract Background Adolescence is marked by increased vulnerability to sleep disturbances and mood disorders. Understanding how day‐to‐day changes in sleep and mood are linked within the same individual is crucial for clarifying sleep's role in emerging internalizing disorders. However, the extent to which an adolescent's fluctuations in sleep predict
Konstantin Drexl +4 more
wiley +1 more source
Genetics Selection Evolution, 2019 Background Hamiltonian Monte Carlo is one of the algorithms of the Markov chain Monte Carlo method that uses Hamiltonian dynamics to propose samples that follow a target distribution.
Motohide Nishio, Aisaku Arakawa
doaj +1 more source
Nonasymptotic bounds on the mean square error for MCMC estimates via renewal techniques [PDF]
, 2011 The Nummellin’s split chain construction allows to decompose a Markov chain Monte Carlo (MCMC) trajectory into i.i.d. "excursions". Regenerative MCMC algorithms based on this technique use a random number of samples.
Miasojedow, Błażej +2 more
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The Journal of Wildlife Management, EarlyView.We used 4 sampling methods to estimate or index the abundance and sex ratio of spotted salamanders (Ambystoma maculatum) over 14 years. The present study highlights the importance of considering individual heterogeneity in capture probability when estimating abundance of pond‐breeding amphibians from capture data with imperfect detection. Abstract Long‐
Patrick D. Moldowan +3 more
wiley +1 more source
Identifying High‐Risk Children Safe for Same‐Day Discharge After Tonsillectomy
The Laryngoscope, EarlyView.ABSTRACT Objective Current guidelines recommend overnight admission for children with severe obstructive sleep apnea (OSA) and obesity undergoing tonsillectomy, although most have uneventful postoperative courses. We aimed to identify low‐risk subgroups within this high‐risk population who may be candidates for same‐day discharge. Methods Retrospective
Amy Ho +9 more
wiley +1 more source
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