Advanced Science, EarlyView.We develop a ligand engineering strategy to construct Ni‐diketimine‐linked COFs with precisely tuned Ni electronic states. By varying the coordinated diketone units, we achieve an optimal electronic configuration that enhances O2 adsorption and lowers the energy barrier for •O2− generation.
Guihong Wu +5 more
wiley +1 more source
Efficient Estimation Methods for the QR Distribution with Type-II Censored Data: An Empirical Validation on Lung Cancer Prognosis. [PDF]
Entropy (Basel)Ramzan Q, Amin M, Alghamdi S, Alharbi R.
europepmc +1 more source
Activity Recognition from Daily-Life Sounds Using Unsupervised Learning with Dirichlet Multinomial Mixture Models. [PDF]
Sensors (Basel)Sadohara K, Miyata N.
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Bayesian inference of interval-censored data with an application to HIV population surveys: a simulation study comparing Hamiltonian Monte Carlo and Metropolis-Hastings sampling algorithms. [PDF]
BMC Med Res Methodolvan Twisk A, Maposa I.
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Protocol for using treeLFA to infer multimorbidity patterns in the form of disease topics from diagnosis data in biobanks. [PDF]
STAR ProtocZhang Y, Jiang X, McVean G, Lunter G.
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Scalable Bayesian Image-on-Scalar Regression for Population-Scale Neuroimaging Data Analysis. [PDF]
J Am Stat AssocXu Y, Johnson TD, Nichols TE, Kang J.
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Quantifying genetic load through joint modelling of inbreeding depression and inbreeding load for litter size in rabbits. [PDF]
Genet Sel EvolHervás-Rivero C +6 more
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On the Geometric Convergence of the Gibbs Sampler
Journal of the Royal Statistical Society Series B: Methodological, 1994 SUMMARY The rate of convergence of the Gibbs sampler is discussed. The Gibbs sampler is a Monte Carlo simulation method with extensive application to computational issues in the Bayesian paradigm. Conditions for the geometric rate of convergence of the algorithm for discrete and continuous parameter spaces are derived, and an ...
Nicholas G Polson, Gareth O Roberts
exaly +3 more sources
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AbstractThe Gibbs sampler is a simple but very powerful algorithm used to simulate from a complex high‐dimensional distribution. It is particularly useful in Bayesian analysis when a complex Bayesian model involves a number of model parameters and the conditional posterior distribution of each component given the others can be derived as a standard ...
Taeyoung Park, Seunghan Lee
openaire +1 more source