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bayesian model in r  (STATA Corporation)


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    STATA Corporation bayesian model in r
    Bayesian Model In R, supplied by STATA Corporation, used in various techniques. Bioz Stars score: 99/100, based on 35303 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/bayesian+model/STATA+16%2E0/pmc12814904-94-13-9
    Average 99 stars, based on 35303 article reviews
    bayesian model in r - by Bioz Stars, 2026-09
    99/100 stars

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    Article Title: Rural-Urban Differences in the Association Between Reproductive Coercion and Postpartum Family Planning.
    Article Snippet: All analyses were conducted using Stata version 16.1 (StataCorp 2019).

    Article Title: Socioeconomic and regional determinants of optimal antenatal care utilization among women in South and Central Somalia
    Article Snippet: The SDHS 2020 data were analyzed using STATA 16.

    Article Title: First dual-frame telephone survey for non-communicable disease risk and protective factors: Methods and main findings from a central Brazilian state, 2022
    Article Snippet: All analyses considered a 5 % significance level and were conducted using the survey module for complex samples in Stata version 16.0.

    Article Title: Drug resistance pulmonary tuberculosis among prisoners in Ethiopia: A systematic review and meta -analysis
    Article Snippet: Stata version 16 (Stata Corp LP, College Station, TX) was used for analysis.

    Software:

    Article Title: Drug resistance pulmonary tuberculosis among prisoners in Ethiopia: A systematic review and meta -analysis
    Article Snippet: .. Meta-analysis was computed using STATA version 16 software. ..

    Article Title: Hepatitis B virus testing, vaccination, and hepatitis B surface antigen prevalence among adult inpatients at an urban polyclinic in Accra, Ghana: a cross-sectional study
    Article Snippet: .. The data were analysed using STATA version 16.0 (Stata Statistical Software: Release 16; StataCorp LLC, College Station, Texas, USA). ..



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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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