Hierarchical Bayesian Regression for Multiple Correlated Exposures: An Application to Prenatal Phthalate Metabolites and Length of Gestation

Epidemiology ◽  
2011 ◽  
Vol 22 ◽  
pp. S128
Author(s):  
Allan Just ◽  
Robin Whyatt ◽  
Qixuan Chen
Keyword(s):  
Bayesian Regression ◽  
Hierarchical Bayesian ◽  
Phthalate Metabolites ◽  
Correlated Exposures ◽  
Length Of Gestation

Driving behavior at a roundabout: A hierarchical Bayesian regression analysis

2014 ◽  
Vol 26 ◽  
pp. 20-26 ◽  
Author(s):  
Abhisek Mudgal ◽  
Shauna Hallmark ◽  
Alicia Carriquiry ◽  
Konstantina Gkritza
Keyword(s):  
Regression Analysis ◽  
Driving Behavior ◽  
Bayesian Regression ◽  
Hierarchical Bayesian

scholarly journals Hierarchical Bayesian Regression for Multi-site Normative Modeling of Neuroimaging Data

2020 ◽  
pp. 699-709
Author(s):  
Seyed Mostafa Kia ◽  
Hester Huijsdens ◽  
Richard Dinga ◽  
Thomas Wolfers ◽  
Maarten Mennes ◽  
...  
Keyword(s):  
Bayesian Regression ◽  
Hierarchical Bayesian ◽  
Neuroimaging Data

scholarly journals Association of urinary concentrations of early pregnancy phthalate metabolites and bisphenol A with length of gestation

2019 ◽  
Vol 18 (1) ◽  
Author(s):  
Helen B. Chin ◽  
Anne Marie Jukic ◽  
Allen J. Wilcox ◽  
Clarice R. Weinberg ◽  
Kelly K. Ferguson ◽  
...  
Keyword(s):  
Bisphenol A ◽  
Early Pregnancy ◽  
Phthalate Metabolites ◽  
Length Of Gestation

scholarly journals Forecasting Mood in Bipolar Disorder From Smartphone Self-assessments: Hierarchical Bayesian Approach

10.2196/15028 ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. e15028 ◽  
Author(s):  
Jonas Busk ◽  
Maria Faurholt-Jepsen ◽  
Mads Frost ◽  
Jakob E Bardram ◽  
Lars Vedel Kessing ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Bipolar Disorder ◽  
Time Series ◽  
Randomized Clinical Trial ◽  
Cross Validation ◽  
Bayesian Regression ◽  
Forecast Errors ◽  
Hierarchical Bayesian ◽  
Validation Experiment ◽  
Self Assessments

Background Bipolar disorder is a prevalent mental health condition that is imposing significant burden on society. Accurate forecasting of symptom scores can be used to improve disease monitoring, enable early intervention, and eventually help prevent costly hospitalizations. Although several studies have examined the use of smartphone data to detect mood, only few studies deal with forecasting mood for one or more days. Objective This study aimed to examine the feasibility of forecasting daily subjective mood scores based on daily self-assessments collected from patients with bipolar disorder via a smartphone-based system in a randomized clinical trial. Methods We applied hierarchical Bayesian regression models, a multi-task learning method, to account for individual differences and forecast mood for up to seven days based on 15,975 smartphone self-assessments from 84 patients with bipolar disorder participating in a randomized clinical trial. We reported the results of two time-series cross-validation 1-day forecast experiments corresponding to two different real-world scenarios and compared the outcomes with commonly used baseline methods. We then applied the best model to evaluate a 7-day forecast. Results The best performing model used a history of 4 days of self-assessment to predict future mood scores with historical mood being the most important predictor variable. The proposed hierarchical Bayesian regression model outperformed pooled and separate models in a 1-day forecast time-series cross-validation experiment and achieved the predicted metrics, R2=0.51 and root mean squared error of 0.32, for mood scores on a scale of −3 to 3. When increasing the forecast horizon, forecast errors also increased and the forecast regressed toward the mean of data distribution. Conclusions Our proposed method can forecast mood for several days with low error compared with common baseline methods. The applicability of a mood forecast in the clinical treatment of bipolar disorder has also been discussed.

scholarly journals Improving hydrological projection performance under contrasting climatic conditions using spatial coherence through a hierarchical Bayesian regression framework

2019 ◽  
Author(s):  
Zhengke Pan ◽  
Pan Liu ◽  
Shida Gao ◽  
Jun Xia ◽  
Jie Chen ◽  
...  
Keyword(s):  
Spatial Coherence ◽  
Model Performance ◽  
Climatic Conditions ◽  
Bayesian Regression ◽  
Model Parameters ◽  
Time Varying ◽  
Hierarchical Bayesian ◽  
Hydrological Models ◽  
Varying Parameters ◽  
Time Varying Parameters

Abstract. Understanding the projection performance of hydrological models under contrasting climatic conditions supports robust decision making, which highlights the need to adopt time-varying parameters in hydrological modeling to reduce the performance degradation. Many existing literatures model the time-varying parameters as functions of physically-based covariates; however, a major challenge remains finding effective information to control the large uncertainties that are linked to the additional parameters within the functions. This paper formulated the time-varying parameters for a lumped hydrological model as explicit functions of temporal covariates and used a hierarchical Bayesian (HB) framework to incorporate the spatial coherence of adjacent catchments to improve the robustness of the projection performance. Four modeling scenarios with different spatial coherence schemes, and one scenario with a stationary scheme for model parameters, were used to explore the transferability of hydrological models under contrasting climatic conditions. Three spatially adjacent catchments in southeast Australia were selected as case studies to examine validity of the proposed method. Results showed that (1) the time-varying function improved the model performance but also amplified the projection uncertainty compared with stationary setting of model parameters; (2) the proposed HB method successfully reduced the projection uncertainty and improved the robustness of model performance; and (3) model parameters calibrated over dry periods were not suitable for predicting runoff over wet periods because of a large degradation in projection performance. This study improves our understanding of the spatial coherence of time-varying parameters, which will help improve the projection performance under differing climatic conditions.

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