On Estimating Cyber Adversaries’ Capabilities: A Bayesian Model Approach

2010 ◽  
pp. 502-504
Author(s):  
Jianchun Jiang ◽  
Weifeng Chen ◽  
Liping Ding
Keyword(s):  
Bayesian Model ◽  
Model Approach

scholarly journals Glimpsing the Compositions of Sub-Neptune-Size Exoplanets

2013 ◽  
Vol 8 (S299) ◽  
pp. 247-251 ◽  
Author(s):  
Leslie A. Rogers
Keyword(s):  
Bayesian Model ◽  
Preliminary Analysis ◽  
Statistical Approach ◽  
Low Density ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Model ◽  
Transiting Planets ◽  
Current Sample ◽  
Rocky Planets ◽  
Model Approach

AbstractThe growing number of transiting planets with mass constraints opens the possibility of applying a statistical approach to learn about the underlying population of planet compositions. We focus on the intriguing transition between rocky exoplanets and planets with voluminous gas layers, and explore how the current census of sub-Neptune-size exoplanets constrains the maximum radii of rocky planets. We outline a hierarchical Bayesian model approach to infer the fraction of planets that are dense enough to be rocky (as a function of planet radius). A preliminary analysis of the current sample of planets with mass and radius constraints reveals that most planets larger than 1.9 R⊕ are too low density to be comprised of Fe and silicates alone.

scholarly journals Using Ensembles of Cognitive Models to Answer Substantive Questions

2018 ◽  
Author(s):  
Henrik Singmann ◽  
David Kellen ◽  
Eda Mizrak ◽  
Ilke Öztekin
Keyword(s):  
Cognitive Processes ◽  
Bayesian Model ◽  
Ensemble Model ◽  
Resource Model ◽  
Data Set ◽  
Measurement Models ◽  
Selection Step ◽  
Cognitive Measurement ◽  
Fully Bayesian ◽  
Model Approach

Cognitive measurement models decompose observed behavior into latent cognitive processes. For situations with more than one condition, such models allow to test hypotheses on the level of the latent processes. We propose a fully Bayesian ensemble model approach to test hypotheses on the level of the latent processes in situations in which multiple measurement models or model classes exist. In the first step, one needs to perform a Bayesian model selection step comparing the hypotheses within each model class. Aggregating the results of the first step yields ensemble posterior model probabilities. We provide an example for a working memory data set using an ensemble of a resource model and a slots model.

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