This is a preview. Log in through your library . Abstract In hierarchical linear models we often find that group indicator variables at the cluster level are significant predictors for the regression ...
Hierarchical linear and generalized linear models can be fit using Gibbs samplers and Metropolis algorithms; these models, however, often have many parameters, and convergence of the seemingly most ...
What Is A Hierarchical Models? Hierarchical models, also known as hierarchical statistical models, multilevel models or random-effects models, are tools for analysing data with a nested or grouped ...
Figure 1. Comparison of HiCroPL with existing prompting approaches. (a) Most existing methods adopt uni-modal adaptation or isolated multi-modal solutions to fine-tune CLIP. (b) Multi-modal Prompt ...
This course is available on the BSc in Data Science and BSc in Mathematics, Statistics and Business. This course is available as an outside option to students on other programmes where regulations ...
Abstract: In this paper, a hierarchical self-triggered mechanism is designed to address real-time trade-offs between control performance and resource utilization in linear systems, accompanied by ...
Abstract: The present work investigates hierarchical beamforming strategies for millimeter-wave communication systems, focusing on amplitude tapering and sub-array approaches. The research addresses ...
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