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Bayesian hierarchical modeling

Bayesian hierarchical modelling is a statistical model written in multiple levels (hierarchical form) that estimates the parameters of the posterior… Expand
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Papers overview

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Highly Cited
2018
Highly Cited
2018
Meta-learning allows an intelligent agent to leverage prior learning episodes as a basis for quickly improving performance on a… Expand
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2017
2017
As the basis of traffic safety management, crash prediction models have long been a prominent focus in the field of freeway… Expand
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2013
2013
A pseudoproxy comparison is presented for two statistical methods used to derive annual climate field reconstructions (CFRs) for… Expand
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2013
2013
Spatial maps of extreme precipitation are a critical component of flood estimation in hydrological modeling, as well as in the… Expand
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2010
2010
Despite significant advances in nanoscience, current physical models are unable to predict nanomanufacturing processes under… Expand
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2010
2010
Many marine species exhibit temporal variation in individual growth. Yearly variation in growth has been identified for red… Expand
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Highly Cited
2008
Highly Cited
2008
BACKGROUND Introduction Data Sets Bayesian Inference and Modeling Likelihood Models Prior Distributions Posterior Distributions… Expand
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Highly Cited
2008
Highly Cited
2008
Standard practice when analyzing data from different types of experiments is to treat data from each type separately. By… Expand
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Highly Cited
2008
Highly Cited
2008
Natural populations of living organisms often have complex histories consisting of phases of expansion and decline, and the… Expand
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Highly Cited
2005
Highly Cited
2005
Logistic regression models have been used in political science for estimating ideal points of legislators and Supreme Court… Expand
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