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Varian Medical bayesian time series model
Bayesian Time Series Model, supplied by Varian Medical, 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+structural+time+series+model/pm40480103-64-8-14
Average 90 stars, based on 1 article reviews
bayesian time series model - by Bioz Stars, 2026-10
90/100 stars

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Article Title: Effect of short-term (4 weeks) low-calorie diet induced weight loss on beta-cell function in overweight normoglycemic subjects: A quasi-experimental pre-post interventional study
Article Snippet: Glucose, insulin, and C-peptide values during the mixed meal challenge test were used to implement the oral minimal model using the Varian Bayesian Analysis program developed by Eichenlaub et al. [ ], and values of insulin sensitivity, dynamic, static, and total beta-cell responsivity were estimated.

Article Title: Understanding the Recovery of On-Demand Mobility Services in the COVID-19 Era
Article Snippet: The recent advances in this framework have introduced Bayesian statistics to estimate the coefficients of different predictors, which enable a sparse choice among a large set of predictors and generate the corresponding inclusion probability of different predictors (Scott and Varian ).The Bayesian time series (BSTS) model has been applied to explore the factors that influence the initial claims for unemployment (Scott and Varian ), sea level (Przybylowski et al. ), Bitcoin’s price, and stock portfolio (Jammalamadaka et al. ).

Article Title: Influence of fire characteristics on the associations between smoke PM 2.5 exposure and acute cardiorespiratory health events.
Article Snippet: We simulated the expected background PM2.5 with a Bayesian time series model (Scott and Varian, 2013) trained on observations at the EPA monitors, accounting for seasonal and meteorological covariates, to estimate the expected PM2.5 concentrations from other sources.

Article Title: The heterogeneous impact of population mobility on the influent characteristics of wastewater treatment facilities.
Article Snippet: Improving the resilience of wastewater treatment facilities (WWTFs) has never been more important with rising risks of disasters under climate change.. Beyond physical damages, non-physical shocks induced by disasters warrant attention.. Human mobility is a vital mediator in transferring the stresses from extreme events into tangible challenges for urban sewage systems by reshaping influent characteristics.

Article Title: Application of a Bayesian structural time series model for evaluating 11-year variation in pH in the headwaters of the Tama River, Japan
Article Snippet: Understanding the temporal variations that occur in river water pH is vital for the conservation of aquatic organisms in riverine ecosystems.. Temporal variations in river water pH are caused by long-term inter-annual trends and short-term periodic or nonperiodic fluctuations.. However, statistical methods that explicitly and quantitatively distinguish these components in time series pH data analyses have not been applied.

Article Title: FORECASTING USING SARIMA AND BAYESIAN STRUCTURAL TIME SERIES METHOD FOR RANGE SEASONAL TIME
Article Snippet: The Bayesian Structural Time Series (BSTS) model, developed by Steven L. Scott and Hal Varian in 2014 [3], has the advantage of producing time series data that is more structured and often in a probabilistic form.

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Article Title: Quantifying cumulative effectiveness of green stormwater infrastructure in improving water quality.
Article Snippet: • We use an empirical method to study cumulative impacts of GSI on water quality.. • GSI have increased light transmission between 3% and 14% in different watersheds.. • GSI have decreased Chlorophyll a level between 0.5 and 1 μg/L in most watersheds.



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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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