Search Results - "Bayesian hierarchical modeling"

Bayesian hierarchical modelling of rainfall extremes by Lehmann, E., Phatak, Aloke, Soltyk, S., Chia, J., Lau, R., Palmer, M.

“…In this paper, we describe the use of a spatial Bayesian hierarchical model (BHM) for characterising rainfall extremes over a region of interest, using historical records of precipitation data from a network of rainfall stations. …”
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Bayesian hierarchical modeling of the individual hypoglycaemic symptoms’ reporting consistency by Zulkafli, Hani Syahida, Streftaris, George, Gibson, Gavin

“…In this paper, we describe a Bayesian hierarchical model which is able to quantify the consistency of reporting symptoms by individual patient and simultaneously investigate patient-specific covariates affecting the consistency. …”
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Bayesian Hierarchical Modelling of Equipment Reliability in Mining: A Pragmatic Approach by Leadbetter, Ryan K.

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Statistical modelling of rainfall intensity-frequency-duration curves using regional frequency analysis and bayesian hierarchical modelling by Soltyk, S., Leonard, M., Phatak, Aloke, Lehmann, E.

“…In this paper, we examine two methods for modelling extreme rainfall spatially: regional frequency analysis (RFA) and a Bayesian hierarchical model (BHM). We produce IFD estimates from both methods and compare the results. …”
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BHPMF – a hierarchical Bayesian approach to gap-filling and trait prediction for macroecology and functional biogeography by Schrodt, Franziska, Kattge, Jens, Shan, Hanhuai, Fazayeli, Farideh, Joswig, Julia, Banerjee, Arindam, Reichstein, Markus, Bönisch, Gerhard, Diaz, Sandra, Dickie, John, Gillison, Andy, Karpatne, Anuj, Lavorel, Sandra, Leadley, Paul, Wirth, Christian B., Wright, Ian J., Wright, S. Joseph, Reich, Peter B.

Subjects: “…Bayesian hierarchical model; Gap-filling; Imputation; Machine learning; Matrix factorization…”
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Assessing differences in legislators’ revealed preferences: a case study on the 107th U.S. Senate by Lofland, Chelsea L., Rodríguez, Abel, Moser, Scott

“…This paper describes a Bayesian hierarchical model that extends existing spatial voting models to test sharp hypotheses about differences in preferences using posterior probabilities associated with such hypotheses. …”
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Worldwide trends in blood pressure from 1975 to 2015: a pooled analysis of 1479 population-based measurement studies with 19·1 million participants by NCD Risk Factor Collaboration , (NCD-RisC), Leng, Huat Foo

“…Methods For this analysis, we pooled national, subnational, or community population-based studies that had measured blood pressure in adults aged 18 years and older. We used a Bayesian hierarchical model to estimate trends from 1975 to 2015 in mean systolic and mean diastolic blood pressure, and the prevalence of raised blood pressure for 200 countries. …”
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Eddington's demon: Inferring galaxy mass functions and other distributions from uncertain data by Obreschkow, D., Murray, Steven, Robotham, A., Westmeier, T.

“…The MML estimator is identical to, but easier and many orders of magnitude faster to compute than the solution of the exact Bayesian hierarchical modelling of all measurement errors. As a key application, this method can accurately recover the mass function (MF) of galaxies, while simultaneously dealing with observational uncertainties (Eddington bias), complex selection functions and unknown cosmic large-scale structure. …”
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A max-stable process model for rainfall extremes at different accumulation durations by Stephenson, A., Lehmann, E., Phatak, Aloke

“…A common existing approach to modeling rainfall extremes employs a spatial Bayesian hierarchical model, where latent Gaussian processes are specified on distributional parameters in order to pool spatial information. …”
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Spatio-temporal Mapping Of Dengue Disease In Peninsular Malaysia by Abd Naeeim, Nurul Syafiah

“…The models for dengue risk calculation were formed based on Bayesian Hierarchical Model (BHM) for discrete spatio-temporal model and Stochastic Partial Differential Equation (SPDE) for continuous spatio-temporal model and their comparison had been carried out…”
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Regions of High Out-Of-Hospital Cardiac Arrest Incidence and Low Bystander CPR Rates in Victoria, Australia by Straney, L., Bray, Janet, Beck, B., Finn, Judith, Bernard, S., Dyson, K., Lijovic, M., Smith, K.

“…Using ArcGIS (ArcMap 10.0), we linked the location of the arrest using the dispatch coordinates (longitude and latitude) to Victorian Local Government Areas (LGAs). We used Bayesian hierarchical models with random effects on each LGA to provide shrunken estimates of the rates of bystander CPR and the incidence rates. …”
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Bright spots among the world’s coral reefs by Cinner, Joshua E., Huchery, Cindy, Macneill, M. Aaron, Graham, Nicholas A.J., McClanahan, Tim R., Maina, Joseph, Maire, Eva, Kittinger, John N., Hicks, Christina C., Mora, Camilo, Allison, Edward H., D'Agata, Stephanie, Hoey, Andrew, Feary, David A., Crowder, Larry, Williams, Ivor D., Kulbicki, Michel, Vigliola, Laurent, Wantiez, Laurent, Edgar, Graham, Stuart-Smith, Rick D., Sandin, Stuart A., Green, Alison L., Hardt, Marah J., Beger, Maria, Friedlander, Alan, Campbell, Stuart J., Holmes, Katherine E., Wilson, Shaun K., Brokovich, Eran, Brooks, Andrew J., Cruz-Motta, Juan J., Booth, David J., Chabanet, Pascale, Gough, Charlie, Tupper, Mark, Ferse, Sebastian C.A., Sumaila, U. Rashid, Mouillot, David

“…Here we compile data from more than 2,500 reefs worldwide and develop a Bayesian hierarchical model to generate expectations of how standing stocks of reef fish biomass are related to 18 socioeconomic drivers and environmental conditions. …”
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Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4·4 million participants by NCD Risk Factor Collaboration, (NCD-RisC)

“…Methods We pooled data from population-based studies that had collected data on diabetes through measurement of its biomarkers. We used a Bayesian hierarchical model to estimate trends in diabetes prevalence—defi ned as fasting plasma glucose of 7·0 mmol/L or higher, or history of diagnosis with diabetes, or use of insulin or oral hypoglycaemic drugs—in 200 countries and territories in 21 regions, by sex and from 1980 to 2014. …”
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Stroke care trends during COVID-19 pandemic in Zanjan Province, Iran from the CASCADE initiative: statistical analysis plan and preliminary results by Ghoreishi, Abdoreza, Arsang-Jang, Shahram, Sabaa-Ayoun, Ziad, Yassi, Nawaf, Sylaja, P. N., Akbari, Yama, Divani, Afshin A., Biller, Jose, Phan, Thanh, Steinwender, Sandy, Silver, Brian, Zand, Ramin, Basri, Hamidon, Iqbal, Omer M., Ranta, Annemarei, Ruland, Sean, Macri, Elizabeth, Ma, Henry, Nguyen, Thanh N., Abootalebi, Shahram, Azarpazhooh, M. Reza

“…From February 18, 2019, to July 18, 2020, we followed ischemic and hemorrhagic stroke hospitalization rates and outcomes in Valiasr Hospital, Zanjan, Iran. We used a Bayesian hierarchical model and an interrupted time series analysis (ITS) to identify changes in stroke hospitalization rate, baseline stroke severity [measured by the National Institutes of Health Stroke Scale (NIHSS)], disability [measured by the modified Rankin Scale (mRS)], presentation time (last seen normal to hospital presentation), thrombolytic therapy rate, median door-to-needle time, length of hospital stay, and in-hospital mortality. …”
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Worldwide trends in diabetes since 1980: a pooled analysis of 751 population - based studies with 4.4 million participants by Gyanchand Rampal, Lekhraj Rampal

“…Methods: We pooled data from population-based studies that had collected data on diabetes through measurement of its biomarkers. We used a Bayesian hierarchical model to estimate trends in diabetes prevalence—defined as fasting plasma glucose of 7·0 mmol/L or higher, or history of diagnosis with diabetes, or use of insulin or oral hypoglycaemic drugs—in 200 countries and territories in 21 regions, by sex and from 1980 to 2014. …”
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