Stability evaluation in process mean using Bayesian statistics and information theory

2021 ◽  
Author(s):  
Yasuhiko Takemoto ◽  
Ikuo Arizono
Keyword(s):  
Information Theory ◽  
Bayesian Statistics ◽  
Stability Evaluation ◽  
Process Mean

scholarly journals COSMOLOGICAL MODEL SELECTION

2008 ◽  
Vol 23 (06) ◽  
pp. 787-802 ◽  
Author(s):  
PIA MUKHERJEE ◽  
DAVID PARKINSON
Keyword(s):  
Information Theory ◽  
Model Selection ◽  
Cosmological Model ◽  
Bayesian Statistics ◽  
Recent Progress ◽  
Bayesian Framework ◽  
Selection Model ◽  
Selection Methods ◽  
Primary Model ◽  
Cosmological Data

We give an overview of the recent progress in the field of cosmological model selection. Model selection statistics, such as those based on information theory and on Bayesian statistics are introduced and discussed. In the Bayesian framework, the marginalised model likelihood, or evidence, is the primary model selection statistic. We describe different methods of computing the evidence, and focus in particular on Nested Sampling. We describe the results of applying model selection methods to new cosmological data such as the CMB measurements by WMAP.

Constructing and Deconstructing Concepts

2016 ◽  
Vol 63 (5) ◽  
pp. 249-262 ◽  
Author(s):  
Charles A. Doan ◽  
Ronaldo Vigo
Keyword(s):  
Information Theory ◽  
Model Performance ◽  
Unsupervised Classification ◽  
Family Resemblance ◽  
Category Membership ◽  
Formal Models ◽  
Human Beings ◽  
One Dimensional ◽  
Relational Patterns ◽  
Optimal Classification

Abstract. Several empirical investigations have explored whether observers prefer to sort sets of multidimensional stimuli into groups by employing one-dimensional or family-resemblance strategies. Although one-dimensional sorting strategies have been the prevalent finding for these unsupervised classification paradigms, several researchers have provided evidence that the choice of strategy may depend on the particular demands of the task. To account for this disparity, we propose that observers extract relational patterns from stimulus sets that facilitate the development of optimal classification strategies for relegating category membership. We conducted a novel constrained categorization experiment to empirically test this hypothesis by instructing participants to either add or remove objects from presented categorical stimuli. We employed generalized representational information theory (GRIT; Vigo, 2011b , 2013a , 2014 ) and its associated formal models to predict and explain how human beings chose to modify these categorical stimuli. Additionally, we compared model performance to predictions made by a leading prototypicality measure in the literature.

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