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Intelligent Smoothing Using Hierarchical Bayesian Models

Epidemiology, 2008
Hierarchical Bayesian modeling provides a flexible approach to modeling in multiparameter problems. Examples include disease mapping and spatiotemporal analysis, and multiple exposure modeling. A key feature of hierarchical Bayesian models is that prior expectations regarding model structure are embedded in a probability model that reflects uncertainty
P. Graham
openaire   +3 more sources

A dynamic hierarchical Bayesian approach for forecasting vegetation condition [PDF]

Natural Hazards and Earth System Sciences, 2022
Agricultural drought, which occurs due to a significant reduction in the moisture required for vegetation growth, is the most complex amongst all drought categories.
E. E. Salakpi, P. D. Hurley, P. D. Hurley, J. M. Muthoka, A. Bowell, A. Bowell, S. Oliver, S. Oliver, P. Rowhani   +8 more
doaj   +1 more source

fishStan: Hierarchical Bayesian models for fisheries

Journal of Open Source Software, 2022
Fisheries managers and ecologists use statistical models to estimate population-level relations and demographic rates (e.g., length-maturity curves, growth curves, and mortality rates).
R. Erickson, D. Stich, Jillian L. Hebert
semanticscholar   +1 more source

Hierarchical change-point regression models including random effects to estimate empirical critical loads for nitrogen using Bayesian Regression Models (brms) and JAGS

MethodsX, 2022
The concept of critical loads is used in the framework of the Convention on Long-range Transboundary Air Pollution (UNECE) to define thresholds below which no damaging effects on habitats occur based on the latest scientific knowledge.
Tobias Roth, Simon Tresch, Enzai Du, Sabine Braun   +3 more
doaj   +1 more source

Sparse reconstructions from few noisy data: analysis of hierarchical Bayesian models with generalized gamma hyperpriors

Inverse Problems, 2020
Solving inverse problems with sparsity promoting regularizing penalties can be recast in the Bayesian framework as finding a maximum a posteriori (MAP) estimate with sparsity promoting priors. In the latter context, a computationally convenient choice of
D. Calvetti, Monica Pragliola, E. Somersalo, A. Strang   +3 more
semanticscholar   +1 more source

Multilevel selection as Bayesian inference, major transitions in individuality as structure learning [PDF]

Royal Society Open Science, 2019
Complexity of life forms on the Earth has increased tremendously, primarily driven by subsequent evolutionary transitions in individuality, a mechanism in which units formerly being capable of independent replication combine to form higher-level ...
Dániel Czégel, István Zachar, Eörs Szathmáry   +2 more
doaj   +1 more source

Application of Multiple Imputation for Missing Values in Three-Way Three-Mode Multi-Environment Trial Data. [PDF]

PLoS ONE, 2015
It is a common occurrence in plant breeding programs to observe missing values in three-way three-mode multi-environment trial (MET) data. We proposed modifications of models for estimating missing observations for these data arrays, and developed a ...
Ting Tian, Geoffrey J McLachlan, Mark J Dieters, Kaye E Basford   +3 more
doaj   +1 more source

Bayesian hierarchical models and prior elicitation for fitting psychometric functions

Frontiers in Computational Neuroscience, 2023
Our previous articles demonstrated how to analyze psychophysical data from a group of participants using generalized linear mixed models (GLMM) and two-level methods.
Maura Mezzetti, Colleen P. Ryan, Colleen P. Ryan, Priscilla Balestrucci, Francesco Lacquaniti, Francesco Lacquaniti, Alessandro Moscatelli, Alessandro Moscatelli   +7 more
doaj   +1 more source

Software for Bayesian Statistics

Journal of Statistical Software, 2021
In this summary we introduce the papers published in the special issue on Bayesian statistics. This special issue comprises 20 papers on Bayesian statistics and Bayesian inference on different topics such as general packages for hierarchical linear model
Michela Cameletti, Virgilio Gómez-Rubio
doaj   +1 more source

Predicting Verbal Learning and Memory Assessments of Older Adults Using Bayesian Hierarchical Models

Frontiers in Psychology, 2022
Verbal learning and memory summaries of older adults have usually been used to describe neuropsychiatric complaints. Bayesian hierarchical models are modern and appropriate approaches for predicting repeated measures data where information ...
Endris Assen Ebrahim, Endris Assen Ebrahim, Mehmet Ali Cengiz   +2 more
doaj   +1 more source