Causal Heterogeneity in Comparative Research: A Bayesian Hierarchical Modelling Approach

1998 ◽  
Vol 42 (4) ◽  
pp. 1233 ◽  
Author(s):  
Bruce Western
Keyword(s):  
Comparative Research ◽  
Bayesian Hierarchical ◽  
Hierarchical Modelling ◽  
Bayesian Hierarchical Modelling ◽  
Modelling Approach

scholarly journals Relationships between anaemia and parasitic infections in Kenyan schoolchildren: A Bayesian hierarchical modelling approach

2008 ◽  
Vol 38 (14) ◽  
pp. 1663-1671 ◽  
Author(s):  
Artemis Koukounari ◽  
Benson B.A. Estambale ◽  
J. Kiambo Njagi ◽  
Bonnie Cundill ◽  
Anthony Ajanga ◽  
...  
Keyword(s):  
Parasitic Infections ◽  
Bayesian Hierarchical ◽  
Hierarchical Modelling ◽  
Bayesian Hierarchical Modelling ◽  
Modelling Approach

scholarly journals Subnational analysis of the COVID-19 epidemic in Brazil

2020 ◽  
Author(s):  
Thomas A Mellan ◽  
Henrique H Hoeltgebaum ◽  
Swapnil Mishra ◽  
Charlie Whittaker ◽  
Ricardo P Schnekenberg ◽  
...  
Keyword(s):  
State Level ◽  
Bayesian Hierarchical ◽  
Hierarchical Modelling ◽  
Bayesian Hierarchical Modelling ◽  
The World ◽  
Asynchronous State ◽  
Subnational Analysis ◽  
The Impact ◽  
Pharmaceutical Interventions ◽  
Modelling Approach

Brazil is currently reporting the second highest number of COVID-19 deaths in the world. Here we characterise the initial dynamics of COVID-19 across the country and assess the impact of non-pharmaceutical interventions (NPIs) that were implemented using a semi-mechanistic Bayesian hierarchical modelling approach. Our results highlight the significant impact these NPIs had across states, reducing an average R_t >3 to an average of 1.5 by 9-May-2020, but that these interventions failed to reduce R_t <1, congruent with the worsening epidemic Brazil has experienced since. We identify extensive heterogeneity in the epidemic trajectory across Brazil, with the estimated number of days to reach 0.1 % of the state population infected since the first nationally recorded case ranging from 20 days in São Paulo compared to 60 days in Goiás, underscoring the importance of sub-national analyses in understanding asynchronous state-level epidemics underlying the national spread and burden of COVID-19.

Data-driven estimate of past and present surface loading over North America: Bayesian Hierarchical Modelling approach applied to GPS and GRACE observations

2021 ◽  
Author(s):  
Yann Ziegler ◽  
Bramha Dutt Vishwakarma ◽  
Aoibheann Brady ◽  
Stephen Chuter ◽  
Sam Royston ◽  
...  
Keyword(s):  
North America ◽  
Hydrological Cycle ◽  
Elastic Solid ◽  
Data Driven ◽  
Balance Model ◽  
Surface Loading ◽  
Bayesian Hierarchical ◽  
Hierarchical Modelling ◽  
Vertical Land Motion ◽  
Bayesian Hierarchical Modelling

&lt;p&gt;Glacial Isostatic Adjustment (GIA) and the hydrological cycle are both associated with mass changes, which are observed by GRACE, and vertical land motion (VLM), which is observed by GPS. Hydrology-related VLM results from the instantaneous response of the elastic solid Earth to surface loading by freshwater, whereas GIA-related VLM reveals the long-term response of the visco-elastic Earth mantle to past glacial cycles. Thus, observations of mass changes and VLM are interrelated and GIA and hydrology are difficult to investigate independently. Taking advantage of the differences in the spatio-temporal characteristics of the GIA and hydrology fields, we can separate the respective contributions of each process. In this work, we use a Bayesian Hierarchical Modelling (BHM) approach to provide a new data-driven estimate of GIA and time-evolving hydrology-related VLM for North America. We detail our processing strategy to prepare the input data for the BHM while preserving the content of the original observations. We discuss the separation of GIA and hydrology processes from a statistical and geophysical point of view. Finally, we assess the reliability of our estimates and compare our results to the latest GIA and hydrological models. Specifically, we compare our GIA solution to a forward-model global field, ICE-6G, and a recent GIA estimate developed for North America (Simon et al. 2017). Our time-evolving hydrology field is compared with WaterGAP, a global water balance model. Overall, for both GIA and hydrology, there is a good agreement between our results and the forward models, but we also find differences which possibly highlight deficiencies in these models.&lt;/p&gt;

scholarly journals Modelling the mass-SFR relation at high redshifts; predicted constraints from JWST

2019 ◽  
Vol 15 (S352) ◽  
pp. 114-114
Author(s):  
Emma Curtis-Lake
Keyword(s):  
Data Sets ◽  
Bayesian Hierarchical ◽  
Hierarchical Modelling ◽  
Bayesian Hierarchical Modelling ◽  
The Galaxy ◽  
Low Mass ◽  
Robust Measurements ◽  
Galaxy Population ◽  
Added Uncertainty ◽  
Intrinsic Scatter

AbstractThe mass-SFR relation of galaxies encodes information of present and historical star formation in the galaxy population. We expect the intrinsic scatter in the relation to increase to low mass where SFR becomes more stochastic. Measurements at z ‰ 4 from the Hubble Frontier fields have hinted at this (Santini et al., 2017), however, with the added uncertainty of lensing magnification we await JWST to provide robust measurements. Even with data-sets provided by JWST, uncertainties on mass and SFR estimates are often large, potentially covariant and dependent on assumptions used. I will present our method of Bayesian hierarchical modelling of the mass-SFR relation that self-consistently propagates uncertainties on mass and SFR estimates to uncertainties on the mass-SFR relation parameters. I will expose the biases imposed by standard SED-modelling practices, and address to what significance we can measure an increase in intrinsic scatter to low masses with JWST.

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