Results 91 to 100 of about 758,750 (320)
Advanced Science, EarlyView.Tracing the evolution from structural regulation to multifunctional integration, this paper systematically analyzes modification strategies for carbon‐based electrodes. It evaluates how element doping, surface functionalization, and composite material design affect the electrode performance, and offers perspectives on future applications and challenges
Yunlei Wang +4 more
wiley +1 more source
A hierarchical Bayesian model for frame representation [PDF]
2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010 In many signal processing problems, it may be fruitful to represent the signal under study in a frame. If a probabilistic approach is adopted, it becomes then necessary to estimate the hyper-parameters characterizing the probability distribution of the frame coefficients.
Chaari, Lotfi +4 more
openaire +7 more sources
Nanozymes Integrated Biochips Toward Smart Detection System
Advanced Science, EarlyView.This review systematically outlines the integration of nanozymes, biochips, and artificial intelligence (AI) for intelligent biosensing. It details how their convergence enhances signal amplification, enables portable detection, and improves data interpretation.
Dongyu Chen +10 more
wiley +1 more source
Scalable Rejection Sampling for Bayesian Hierarchical Models [PDF]
, 2014 Bayesian hierarchical modeling is a popular approach to capturing unobserved heterogeneity across individual units. However, standard estimation methods such as Markov chain Monte Carlo (MCMC) can be impracticable for modeling outcomes from a large ...
Braun, Michael, Damien, Paul
core
Bayesian Estimation in Hierarchical Models [PDF]
, 2015 Bayesian data analysis involves describing data by meaningful mathematical models, and allocating credibility to parameter values that are consistent with the data and with prior knowledge. The Bayesian approach is ideally suited for constructing hierarchical models, which are useful for data structures with multiple levels, such as data from ...
John K. Kruschke, Wolf Vanpaemel
openaire +1 more source
The neural dynamics of hierarchical Bayesian causal inference in multisensory perception
Nature Communications, 2019 Transforming the barrage of sensory signals into a coherent multisensory percept relies on solving the binding problem – deciding whether signals come from a common cause and should be integrated or, instead, segregated.
Tim Rohe, A. Ehlis, U. Noppeney
semanticscholar +1 more source
Advanced Science, EarlyView.This study introduces stVGP, a variational spatial Gaussian process framework for multi‐modal, multi‐slice spatial transcriptomics. By integrating histological and genomic data through hybrid alignment and attention‐based fusion, stVGP reconstructs coherent 3D functional landscapes.
Zedong Wang +3 more
wiley +1 more source
, 2018 With continued advances in Geographic Information Systems and related computational technologies, statisticians are often required to analyze very large spatial datasets.
Cressie N. +6 more
core +1 more source
A Bayesian hierarchical copula model
Electronic Journal of Statistics, 2020 The authors of the paper propose a \textit{Bayesian hierarchical copula model} to accommodate hierarchical structures of dependent data, where the subject-level dependence is modeled by the copula-based model and the hierarchical structure is described using random dependence parameters.
Zhuang, Haoxin +2 more
openaire +3 more sources
Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies
bioRxiv, 2018 Computational modeling plays an important role in modern neuroscience research. Much previous research has relied on statistical methods, separately, to address two problems that are actually interdependent. First, given a particular computational model,
Payam Piray +4 more
semanticscholar +1 more source