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bayesian parameter inference and uncertainty quantification for a computational pulmonary hemodynamics model using gaussian processes  (Edwards Lifesciences Inc)

 
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    Edwards Lifesciences Inc bayesian parameter inference and uncertainty quantification for a computational pulmonary hemodynamics model using gaussian processes
    Bayesian Parameter Inference And Uncertainty Quantification For A Computational Pulmonary Hemodynamics Model Using Gaussian Processes, supplied by Edwards Lifesciences Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/bayesian+model/bayesian+parameter+inference+and+uncertainty+quantification+for+a+computational+pulmonary+hemodynamics+model+using+gaussian+processes/pm40513483-11-13-33
    Average 90 stars, based on 1 article reviews
    bayesian parameter inference and uncertainty quantification for a computational pulmonary hemodynamics model using gaussian processes - by Bioz Stars, 2026-11
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    Article Title: Bayesian parameter inference and uncertainty quantification for a computational pulmonary hemodynamics model using Gaussian processes.
    Article Snippet: .. Bayesian parameter inference and uncertainty quantification for a computational pulmonary hemodynamics model using Gaussian processes Amirreza Kachabi a , Sofia Altieri Correa a, Naomi C. Chesler a , Mitchel J. Colebank a,b,* a Edwards Lifesciences Foundation Cardiovascular Innovation and Research Center, Department of Biomedical Engineering, University of California, Irvine, CA, USA b Department of Mathematics, University of South Carolina, Columbia, SC, USA A R T I C L E I N F O Keywords: Statistical emulation Uncertainty quantification One-dimensional fluid mechanics Hemodynamics CTEPH A B S T R A C T Subject-specific modeling is a powerful tool in cardiovascular research, providing insights beyond the reach of current clinical diagnostics. ..



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    Comparison of 12-month and 33-month ahead forecasts for monthly Hepatitis B incidence in Henan Province, China, 2014–2023, using seasonal autoregressive integrated moving average, seasonal autoregressive integrated moving average with exogenous variables, Bayesian structural time series without Baidu Index, and Bayesian structural time series with Baidu Index models. A Seasonal autoregressive integrated moving average model for 12-month ahead forecasts. B Seasonal autoregressive integrated moving average with exogenous variables model for 12-month ahead forecasts. C Bayesian structural time series model without Baidu Index for 12-month ahead forecasts. D Bayesian structural time series model with Baidu Index for 12-month ahead forecasts. E Seasonal autoregressive integrated moving average model for 33-month ahead forecasts. F Seasonal autoregressive integrated moving average with exogenous variables model for 33-month ahead forecasts. G Bayesian structural time series model without Baidu Index for 33-month ahead forecasts. H Bayesian structural time series model with Baidu Index for 33-month ahead forecasts. The Bayesian structural time series models, especially with Baidu Index, demonstrate superior predictive performance

    Journal: Archives of Public Health

    Article Title: Early warning of hepatitis B epidemics in Henan Province, China, from 2014 to 2023 based on Baidu Index and Bayesian Structural Time Series model

    doi: 10.1186/s13690-026-01837-y

    Figure Lengend Snippet: Comparison of 12-month and 33-month ahead forecasts for monthly Hepatitis B incidence in Henan Province, China, 2014–2023, using seasonal autoregressive integrated moving average, seasonal autoregressive integrated moving average with exogenous variables, Bayesian structural time series without Baidu Index, and Bayesian structural time series with Baidu Index models. A Seasonal autoregressive integrated moving average model for 12-month ahead forecasts. B Seasonal autoregressive integrated moving average with exogenous variables model for 12-month ahead forecasts. C Bayesian structural time series model without Baidu Index for 12-month ahead forecasts. D Bayesian structural time series model with Baidu Index for 12-month ahead forecasts. E Seasonal autoregressive integrated moving average model for 33-month ahead forecasts. F Seasonal autoregressive integrated moving average with exogenous variables model for 33-month ahead forecasts. G Bayesian structural time series model without Baidu Index for 33-month ahead forecasts. H Bayesian structural time series model with Baidu Index for 33-month ahead forecasts. The Bayesian structural time series models, especially with Baidu Index, demonstrate superior predictive performance

    Article Snippet: G Bayesian structural time series model without Baidu Index for 33-month ahead forecasts.

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