Results 171 to 180 of about 15,712,923 (297)
Bayesian Factor Selection in Dynamic Term Structure Models [PDF]
This paper discusses Bayesian procedures for factor selection in dynamic term structure models through simulation methods based on Markov Chain Monte Carlo. The number of factors, besides influencing the fitting and prediction of observed yields, is also
Márcio Laurini
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Advanced Science, EarlyView.This work develops photocatalytic coatings for indoor formaldehyde removal on wood surfaces. It explores coating‐induced color variations and visual perception via eye‐tracking tests. Combined with XGBoost and Bayesian optimization, an intelligent design framework is established to guide the optimization of visually friendly wood photocatalytic ...
Xinyue Wei +8 more
wiley +1 more source
Bayesian sequential D-D optimal model-robust designs. [PDF]
Alphabetic optimal design theory assumes that the model for which the optimal design is derived is usually known. However in real-life applications, this assumption may not be credible, as models are rarely known in advance.
Ruggoo, A, Vandebroek, Martina
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CoSP: Reconfigurable Metamaterial Inverse Design via Contrastive Pretrained Large Language Model
Advanced Science, EarlyView.In this work, CoSP (contrastive multi‐state pretrain), an intelligent inverse design method for reconfigurable metamaterials based on a contrastive pretrained large language model, is proposed. Numerical experiments demonstrate that CoSP can design reconfigurable metamaterial structures for multi‐state, multi‐band optical responses, showing great ...
Shujie Yang +4 more
wiley +1 more source
Bayesian Model Selection for Derived Responses. [PDF]
Stat MedRoy B, Lesaffre E.
europepmc +1 more source
Systematic Multi‐Level Analyses Decode the Arthritis‐Neurodegeneration Axis With In Vivo Validation
Advanced Science, EarlyView.Arthritis and neurodegeneration are usually studied as separate disorders, but this study connects them through population evidence, genetic inference, transcriptomic mapping, and mouse models. It highlights RNF40 as a context‐dependent joint‐brain candidate, induced in inflammatory joints yet functionally linked to dopamine‐neuron vulnerability ...
Jinwen Wang +7 more
wiley +1 more source
Forecast Combination and Bayesian Model Averaging - A Prior Sensitivity Analysis [PDF]
In this study the forecast performance of model averaged forecasts is compared to that of alternative single models. Following Eklund and Karlsson (2007) we form posterior model probabilities - the weights for the combined forecast - based on the ...
Feldkircher, Martin
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Advanced Science, EarlyView.TSTScope is an interpretable AI framework that integrates single‐cell transcriptomes with TCR information through curated gene‐program constraints. By linking receptor context to functional T cell states, it reveals response‐associated tumor‐specific T cell programs in lung cancer immunotherapy cohorts and defines an MPR score associated with ...
Shiwei Cao +8 more
wiley +1 more source
Optimizing representations for integrative structural modeling using Bayesian model selection. [PDF]
BioinformaticsArvindekar S +3 more
europepmc +1 more source
On the effect of prior assumptions in Bayesian model averaging with applications to growth regression [PDF]
This paper examines the problem of variable selection in linear regression models. Bayesian model averaging has become an important tool in empirical settings with large numbers of potential regressors and relatively limited numbers of observations.
Ley, Eduardo, Steel, Mark F. J.
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