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bayesian model-assisted formulation of prim  (CH Instruments)

 
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    CH Instruments bayesian model-assisted formulation of prim
    Bayesian Model Assisted Formulation Of Prim, supplied by CH Instruments, 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+model+assisted+formulation+of+prim/pmc03293195-69-35-77
    Average 90 stars, based on 1 article reviews
    bayesian model-assisted formulation of prim - by Bioz Stars, 2026-10
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    Article Title: Local Sparse Bump Hunting
    Article Snippet: Apart from numerous successful applications of conventional PRIM in many fields (geology, marketing, finance, medicine, bioinformatics, process optimization) there has been only a few extensions of the original work, including a Bayesian model-assisted formulation of PRIM ( Wu and Chipman 2003 ), a boosted version of PRIM based on Adaboost ( Pei Wang et al. 2004 ), an extension of PRIM to censored responses ( LeBlanc et al. 2002 ), and to discrete variables ( Il-Gyo and Chi-Hyuck 2008 ).



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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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