Výsledky vyhledávání - "Brost, Brian M."

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Greater Than the Sum of its Parts: Computationally Flexible Bayesian Hierarchical Modeling

Autor: Johnson, Devin S., Brost, Brian M., Hooten, Mevin B.

We propose a multistage method for making inference at all levels of a Bayesian hierarchical model (BHM) using natural data partitions to increase efficiency by allowing computations to take place in parallel form using software that is most appropri

Externí odkaz: http://arxiv.org/abs/2010.12568

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Making Recursive Bayesian Inference Accessible

Autor: Hooten, Mevin B., Johnson, Devin S., Brost, Brian M.

Bayesian models provide recursive inference naturally because they can formally reconcile new data and existing scientific information. However, popular use of Bayesian methods often avoids priors that are based on exact posterior distributions resul

Externí odkaz: http://arxiv.org/abs/1807.10981

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Hierarchical animal movement models for population-level inference

Autor: Hooten, Mevin B., Buderman, Frances E., Brost, Brian M., Hanks, Ephraim M., Ivan, Jacob S.

New methods for modeling animal movement based on telemetry data are developed regularly. With advances in telemetry capabilities, animal movement models are becoming increasingly sophisticated. Despite a need for population-level inference, animal m

Externí odkaz: http://arxiv.org/abs/1606.09585

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The basis function approach for modeling autocorrelation in ecological data

Autor: Hefley, Trevor J., Broms, Kristin M., Brost, Brian M., Buderman, Frances E., Kay, Shannon L., Scharf, Henry R., Tipton, John R., Williams, Perry J., Hooten, Mevin B.

Analyzing ecological data often requires modeling the autocorrelation created by spatial and temporal processes. Many of the statistical methods used to account for autocorrelation can be viewed as regression models that include basis functions. Unde

Externí odkaz: http://arxiv.org/abs/1606.05658

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