Symbolic Regression Model Comparison Approach Using Transmitted Variation

2013 ◽  
pp. 139-154
Author(s):  
Flor A. Castillo ◽  
Carlos M. Villa ◽  
Arthur K. Kordon
Keyword(s):  
Regression Model ◽  
Model Comparison ◽  
Symbolic Regression ◽  
Comparison Approach ◽  
Transmitted Variation

scholarly journals A Bayesian model comparison approach to test the specificity of visual integration impairment in schizophrenia or psychosis

2018 ◽  
Vol 265 ◽  
pp. 271-278 ◽  
Author(s):  
Tyler B. Grove ◽  
Beier Yao ◽  
Savanna A. Mueller ◽  
Merranda McLaughlin ◽  
Vicki L. Ellingrod ◽  
...  
Keyword(s):  
Bayesian Model ◽  
Model Comparison ◽  
Visual Integration ◽  
Bayesian Model Comparison ◽  
Comparison Approach

scholarly journals Capturing Ordinal Theoretical Constraint in Psychological Science

2018 ◽  
Author(s):  
Julia M. Haaf ◽  
Fayette Klaassen ◽  
Jeffrey Rouder
Keyword(s):  
Social Sciences ◽  
Linear Model ◽  
Bayesian Model ◽  
Model Comparison ◽  
Social Scientists ◽  
Psychological Science ◽  
Bayesian Model Comparison ◽  
Comparison Approach ◽  
The Social ◽  
Ordinal Level

Most theories in the social sciences are verbal and provide ordinal-level predictions for data. For example, a theory might predict that performance is better in one condition than another, but not by how much. One way of gaining additional specificity is to posit many ordinal constraints that hold simultaneously. For example a theory might predict an effect in one condition, a larger effect in another, and none in a third. We show how common theoretical positions naturally lead to multiple ordinal constraints. To assess whether multiple ordinal constraints hold in data, we adopt a Bayesian model comparison approach. The result is an inferential system that is custom-tuned for the way social scientists conceptualize theory, and that is more intuitive and informative than current linear-model approaches.

Assessing the relevance of fMRI-based prior in the EEG inverse problem: a bayesian model comparison approach

2005 ◽  
Vol 53 (9) ◽  
pp. 3461-3472 ◽  
Author(s):  
J. Daunizeau ◽  
C. Grova ◽  
J. Mattout ◽  
G. Marrelec ◽  
D. Clonda ◽  
...  
Keyword(s):  
Inverse Problem ◽  
Bayesian Model ◽  
Model Comparison ◽  
Bayesian Model Comparison ◽  
Comparison Approach ◽  
Eeg Inverse Problem

Data Analysis: A Model-Comparison Approach.

1990 ◽  
Vol 85 (410) ◽  
pp. 600
Author(s):  
Virginia Clark ◽  
Charles M. Judd ◽  
Gary H. McClelland
Keyword(s):  
Data Analysis ◽  
Model Comparison ◽  
Comparison Approach

scholarly journals Searching for rewards in graph-structured spaces

2019 ◽  
Author(s):  
Eric Schulz ◽  
Charley M Wu
Keyword(s):  
Reinforcement Learning ◽  
Regression Model ◽  
Gaussian Process ◽  
Predictive Model ◽  
Human Behavior ◽  
Model Comparison ◽  
Gaussian Process Regression ◽  
Diffusion Kernel ◽  
Function Learning ◽  
Psychological Models

How do people generalize and explore structured spaces? We study human behavior on a multi-armed bandit task, where rewards are influenced by the connectivity structure of a graph. A detailed predictive model comparison shows that a Gaussian Process regression model using a diffusion kernel is able to best describe participant choices, and also predict judgments about expected reward and confidence. This model unifies psychological models of function learning with the Successor Representation used in reinforcement learning, thereby building a bridge between different models of generalization.

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