Results 101 to 110 of about 3,211,049 (333)
Probing the covariance matrix [PDF]
AIP Conference Proceedings, 2006 By drawing an analogy between the logarithm of a probability distribution and a physical potential, it is natural to ask the question, “what is the effect of applying an external force on model parameters?” In Bayesian inference, parameters are frequently estimated as those that maximize the posterior, yielding the maximum a posteriori (MAP) solution ...
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Advanced Science, EarlyView.Using machine learning on a mega‐scale global dataset (n = 1,336,840) reveals a robust personality trait architecture beyond the Big Five. A Big Two model, broadly capturing social engagement and internal mentation, defines a geometric space that links personality to neurocognitive profiles.
Kaixiang Zhuang +7 more
wiley +1 more source
Powers of some one-sided multivariate tests with unknown population covariance matrix [PDF]
Songklanakarin Journal of Science and Technology (SJST), 2009 For a multivariate normal population, Kudo (1963), Shorack (1967) and Perlman (1969) derived the likelihood ratio tests of the null hypothesis that the mean vector is zero with a one-sided alternative for a known covariance matrix, a partially known ...
Samruam Chongcharoen
doaj
Heuristically Adaptive Diffusion‐Model Evolutionary Strategy
Advanced Science, EarlyView.Building on the mathematical equivalence between diffusion models and evolutionary algorithms, researchers demonstrate unprecedented control over evolutionary optimization through conditional diffusion. By training diffusion models to associate parameters with specific traits, they can guide evolution toward solutions exhibiting desired behaviors ...
Benedikt Hartl +3 more
wiley +1 more source
The Astrophysical JournalWe present a method to quantify the convergence rate of the fast estimators of the covariance matrices in the large-scale structure analysis. Our method is based on the Kullback–Leibler (KL) divergence, which describes the relative entropy of two ...
Zhigang Li +3 more
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BAYESIAN INFERENCE FOR A COVARIANCE MATRIX
Conference on Applied Statistics in Agriculture, 2014 Final version, already published in proceedings, Proceedings of 26th Annual Conference on Applied Statistics in Agriculture.
Alvarez, Ignacio +2 more
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Consensus Formation and Change are Enhanced by Neutrality
Advanced Science, EarlyView.Neutral agents are shown to enhance both the formation and overturning of consensus in collective decision‐making. A general mathematical model and experiments with locusts and humans reveal that neutrality enables robust consensus via simple interactions and accelerates consensus change by reducing effective population size.
Andrei Sontag +3 more
wiley +1 more source
Advanced Science, EarlyView.This study investigates how the internal structure of fiber‐reinforced ceramic composites affects their resistance to damage. By combining 3D X‐ray imaging with acoustic emission monitoring during mechanical testing, it reveals how silicon distribution influences crack formation.
Yang Chen +7 more
wiley +1 more source
Covariance, correlation matrix and the multi-scale community structure of networks
, 2010 Empirical studies show that real world networks often exhibit multiple scales of topological descriptions. However, it is still an open problem how to identify the intrinsic multiple scales of networks.
Cheng, Xue-Qi +2 more
core +1 more source
Linearizing and Forecasting: A Reservoir Computing Route to Digital Twins of the Brain
Advanced Science, EarlyView.A new approach uses simple neural networks to create digital twins of brain activity, capturing how different patterns unfold over time. The method generates and recovers key dynamics even from noisy data. When applied to fMRI, it predicts brain signals and reveals distinctive activity patterns across regions and individuals, opening possibilities for ...
Gabriele Di Antonio +3 more
wiley +1 more source