Results 81 to 90 of about 29,218,928 (220)
Correlation‐guided multi‐object tracking with correlation feature transfer
IET Computer Vision, 2019 Here, the authors propose a correlation‐guided Monte Carlo Markov chain (MCMC) solver to promote the efficiency for tracking multiple objects under recursive Bayesian filtering framework.
Jiatong Li, Yanjie Zhao, Zhiguo Jiang
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Precision of methods for calculating identity-by-descent matrices using multiple markers
Genetics Selection Evolution, 2002 A rapid, deterministic method (DET) based on a recursive algorithm and a stochastic method based on Markov Chain Monte Carlo (MCMC) for calculating identity-by-descent (IBD) matrices conditional on multiple markers were compared using stochastic ...
Sørensen Anders +3 more
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Detection of the quality of vital signals by the Monte Carlo Markov Chain (MCMC) method and noise deleting. [PDF]
Health Inf Sci Syst, 2021 Vajargah KF, Benis SG, Golshan HM.
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A splitting method to reduce MCMC variance
, 2020 We explore whether splitting and killing methods can improve the accuracy of Markov chain Monte Carlo (MCMC) estimates of rare event probabilities, and we make three contributions. First, we prove that "weighted ensemble" is the only splitting and killing method that provides asymptotically consistent estimates when combined with MCMC. Second, we prove
Webber, Robert J. +2 more
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Phase randomisation: a convergence diagnostic test for MCMC [PDF]
Most MCMC users address the convergence problem by applying diagnostic tools to the output produced by running their samplers. Potentially useful diagnostics may be borrowed from diverse areas such as time series.
Darfiana Nur +2 more
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Using MCMC methods in some application problems
International Journal of Scientific World, 2014 MCMC methods are very important tools for estimating unknown parameters in Bayesian models. Especially in the case of high dimensions. Gaussian mixture model is one of the applications of estimating hyper parameters by MCMC method. Keywords : Gibbs Sampling, Slice Sampling, Metropolis-Hastings Algorithm, Gaussian, Mixture Model.
Parvin Azhdari +2 more
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Dynamical Sampling with Langevin Normalization Flows
Entropy, 2019 In Bayesian machine learning, sampling methods provide the asymptotically unbiased estimation for the inference of the complex probability distributions, where Markov chain Monte Carlo (MCMC) is one of the most popular sampling methods. However, MCMC can
Minghao Gu, Shiliang Sun, Yan Liu
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Infectious Disease ModellingDisease forecasting and surveillance often involve fitting models to a tremendous volume of historical testing data collected over space and time. Bayesian spatio-temporal regression models fit with Markov chain Monte Carlo (MCMC) methods are commonly ...
Rongjie Huang +5 more
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A Robust Non-Gaussian Data Assimilation Method for Highly Non-Linear Models
Atmosphere, 2018 In this paper, we propose an efficient EnKF implementation for non-Gaussian data assimilation based on Gaussian Mixture Models and Markov-Chain-Monte-Carlo (MCMC) methods.
Elias D. Nino-Ruiz +2 more
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A Markov Chain Monte Carlo Procedure for Efficient Bayesian Inference on the Phase-Type Aging Model
StatsThe phase-type aging model (PTAM) belongs to a class of Coxian-type Markovian models that can provide a quantitative description of well-known aging characteristics that are part of a genetically determined, progressive, and irreversible process.
Cong Nie +3 more
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