scholarly journals Hierarchical Bayesian models in ecology: Reconstructing species interaction networks from non-homogeneous species abundance data

2012 ◽  
Vol 11 ◽  
pp. 55-64 ◽  
Author(s):  
Andrej Aderhold ◽  
Dirk Husmeier ◽  
Jack J. Lennon ◽  
Colin M. Beale ◽  
V. Anne Smith
Keyword(s):  
Bayesian Models ◽  
Species Abundance ◽  
Species Interaction ◽  
Interaction Networks ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Models ◽  
Abundance Data ◽  
Species Abundance Data

scholarly journals Hierarchical Bayesian models for soil CO2flux using soil texture: a case study in central Hokkaido, Japan

2014 ◽  
Vol 61 (1) ◽  
pp. 116-132 ◽  
Author(s):  
Xi Li ◽  
Kiwamu Ishikura ◽  
Chunying Wang ◽  
Jagadeesh Yeluripati ◽  
Ryusuke Hatano
Keyword(s):  
Soil Texture ◽  
Bayesian Models ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Models

scholarly journals Should we be concerned about multiple comparisons in hierarchical Bayesian models?

2019 ◽  
Vol 10 (4) ◽  
pp. 553-564 ◽  
Author(s):  
Kiona Ogle ◽  
Drew Peltier ◽  
Michael Fell ◽  
Jessica Guo ◽  
Heather Kropp ◽  
...  
Keyword(s):  
Bayesian Models ◽  
Multiple Comparisons ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Models

Hierarchical Bayesian Models

2015 ◽  
Author(s):  
N. Thompson Hobbs ◽  
Mevin B. Hooten
Keyword(s):  
Bayesian Networks ◽  
Hierarchical Models ◽  
Bayesian Models ◽  
Research Problem ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Models ◽  
Broad Application ◽  
Joint Distributions ◽  
Definition Of

This chapter seeks to explain hierarchical models and how they differ from simple Bayesian models and to illustrate building hierarchical models using mathematically correct expressions. It begins with the definition of hierarchical models. Next, the chapter introduces four general classes of hierarchical models that have broad application in ecology. These classes can be used individually or in combination to attack virtually any research problem. Examples are used to show how to draw Bayesian networks that portray stochastic relationships between observed and unobserved quantities. The chapter furthermore shows how to use network drawings as a guide for writing posterior and joint distributions.

Comparing two multinomial samples using hierarchical Bayesian models

2019 ◽  
Vol 9 (2) ◽  
pp. 145-154
Author(s):  
A. R. Masegosa ◽  
A. Torres ◽  
M. Morales ◽  
A. Salmerón
Keyword(s):  
Bayesian Models ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Models
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