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   Articles with "bayesian regression" as a keyword



Learning models for electron densities with Bayesian regression

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recommendations! Published in 2018 at "Computational Materials Science"

DOI: 10.1016/j.commatsci.2018.03.029

Abstract: Abstract The Hohenberg-Kohn theorems posit the ground state electron density as a property of fundamental importance in condensed matter physics, finding widespread application in much of solid state physics in the form of density functional… read more here.

Keywords: learning models; physics; densities bayesian; models electron ... See more keywords

Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis.

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recommendations! Published in 2024 at "Journal of chemical theory and computation"

DOI: 10.1021/acs.jctc.4c00124

Abstract: We propose a novel approach to evaluating the ionic Seebeck coefficient in electrolytes from relatively short equilibrium molecular dynamics simulations, based on the Green-Kubo theory of linear response and Bayesian regression analysis. By exploiting the… read more here.

Keywords: seebeck; seebeck coefficient; bayesian regression; regression analysis ... See more keywords
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Bayesian regression analysis of stutter in DNA mixtures

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recommendations! Published in 2020 at "Communications in Statistics - Theory and Methods"

DOI: 10.1080/03610926.2019.1710760

Abstract: Abstract Probabilistic genotyping methods use a hierarchical probability model in deconvolution of DNA mixtures. The parameters of the model, including the stutter which are required to calculate the expected values of peak heights, are estimated… read more here.

Keywords: regression analysis; dna; analysis stutter; dna mixtures ... See more keywords

Influential Observations in Bayesian Regression Tree Models

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recommendations! Published in 2022 at "Journal of Computational and Graphical Statistics"

DOI: 10.1080/10618600.2023.2210180

Abstract: BCART (Bayesian Classification and Regression Trees) and BART (Bayesian Additive Regression Trees) are popular Bayesian regression models widely applicable in modern regression problems. Their popularity is intimately tied to the ability to flexibly model complex… read more here.

Keywords: regression; bayesian regression; tree models; regression tree ... See more keywords

A data-adaptive Bayesian regression approach for polygenic risk prediction

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recommendations! Published in 2022 at "Bioinformatics"

DOI: 10.1093/bioinformatics/btac024

Abstract: Abstract Motivation Polygenic risk score (PRS) has been widely exploited for genetic risk prediction due to its accuracy and conceptual simplicity. We introduce a unified Bayesian regression framework, NeuPred, for PRS construction, which accommodates varying… read more here.

Keywords: polygenic risk; risk prediction; bayesian regression; prediction ... See more keywords

Bayesian regression and model selection for isothermal titration calorimetry with enantiomeric mixtures

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recommendations! Published in 2022 at "PLoS ONE"

DOI: 10.1371/journal.pone.0273656

Abstract: Bayesian regression is performed to infer parameters of thermodynamic binding models from isothermal titration calorimetry measurements in which the titrant is an enantiomeric mixture. For some measurements the posterior density is multimodal, indicating that additional… read more here.

Keywords: titration calorimetry; bayesian regression; model selection; isothermal titration ... See more keywords

Developing a Predictive Model for Depressive Disorders Using Stacking Ensemble and Naive Bayesian Nomogram: Using Samples Representing South Korea

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recommendations! Published in 2021 at "Frontiers in Psychiatry"

DOI: 10.3389/fpsyt.2021.773290

Abstract: This study provided baseline data for preventing depression in female older adults living alone by understanding the degree of their depressive disorders and factors affecting these depressive disorders by analyzing epidemiological survey data representing South… read more here.

Keywords: study; nomogram; stacking ensemble; bayesian regression ... See more keywords

A Closed-Loop Scheduling Framework for Prefabricated Bridge Girders: Bayesian Regression and TCTO-Based Optimization

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recommendations! Published in 2025 at "Buildings"

DOI: 10.3390/buildings15224168

Abstract: Prefabricated construction has emerged as a key strategy to enhance productivity and quality in infrastructure projects. Yet, construction scheduling for prefabricated infrastructure projects often suffers from persistent discrepancies between planned and actual performance due to… read more here.

Keywords: bayesian regression; performance; closed loop; framework ... See more keywords