Comorbid science?

2010 ◽  
Vol 33 (2-3) ◽  
pp. 153-155 ◽  
Author(s):  
David Danks ◽  
Stephen Fancsali ◽  
Clark Glymour ◽  
Richard Scheines
Keyword(s):  
Causal Inference ◽  
Latent Variable ◽  
Preliminary Analysis ◽  
Latent Variable Models ◽  
Causal Relationships

AbstractWe agree with Cramer et al.'s goal of the discovery of causal relationships, but we argue that the authors' characterization of latent variable models (as deployed for such purposes) overlooks a wealth of extant possibilities. We provide a preliminary analysis of their data, using existing algorithms for causal inference and for the specification of latent variable models.

scholarly journals Performance Evaluation in Machine Learning: The Good, the Bad, the Ugly, and the Way Forward

2019 ◽  
Vol 33 ◽  
pp. 9808-9814 ◽  
Author(s):  
Peter Flach
Keyword(s):  
Machine Learning ◽  
Performance Evaluation ◽  
Causal Inference ◽  
Latent Variable ◽  
Measurement Theory ◽  
Latent Variable Models ◽  
Data Set ◽  
Evaluation Measures ◽  
Classifier Evaluation ◽  
The Way

This paper gives an overview of some ways in which our understanding of performance evaluation measures for machine-learned classifiers has improved over the last twenty years. I also highlight a range of areas where this understanding is still lacking, leading to ill-advised practices in classifier evaluation. This suggests that in order to make further progress we need to develop a proper measurement theory of machine learning. I then demonstrate by example what such a measurement theory might look like and what kinds of new results it would entail. Finally, I argue that key properties such as classification ability and data set difficulty are unlikely to be directly observable, suggesting the need for latent-variable models and causal inference.

scholarly journals Toward an Integrative Psychometric Model of Emotions

2019 ◽  
Author(s):  
Jens Lange ◽  
Jonas Dalege ◽  
Denny Borsboom ◽  
Gerben van Kleef ◽  
Agneta Fischer
Keyword(s):  
Network Model ◽  
Latent Variable ◽  
Latent Variable Models ◽  
Human Condition ◽  
Causal Relationships ◽  
Theoretical Perspectives ◽  
Psychometric Models ◽  
Psychometric Model ◽  
The Human Condition

Emotions are part and parcel of the human condition, but their nature is debated. Three broad classes of theories about the nature of emotions can be distinguished: affect program theories, constructionist theories, and appraisal theories. Integrating them in a unifying theory is challenging. An integrative psychometric model of emotions can inform such a theory, because psychometric models are intertwined with theoretical perspectives about constructs. To identify an integrative psychometric model, we (a) delineate properties of emotions stated by emotion theories, and (b) investigate whether psychometric models account for these properties. Specifically, an integrative psychometric model of emotions should allow identifying distinct emotions (central in affect program theories), should allow between and within person variation of emotions (central in constructionist theories), and should allow causal relationships between emotion components (central in appraisal theories). Evidence suggests that the popular reflective and formative latent variable models—in which emotions are conceptualized as unobservable causes or consequences of emotion components—cannot account for all properties. Conversely, a psychometric network model—in which emotions are conceptualized as systems of causally interacting emotion components—accounts for all properties. The psychometric network model thus constitutes an integrative psychometric model of emotions, facilitating progress toward a unifying theory.

scholarly journals Toward an Integrative Psychometric Model of Emotions

2020 ◽  
Vol 15 (2) ◽  
pp. 444-468 ◽  
Author(s):  
Jens Lange ◽  
Jonas Dalege ◽  
Denny Borsboom ◽  
Gerben A. van Kleef ◽  
Agneta H. Fischer
Keyword(s):  
Network Model ◽  
Latent Variable ◽  
Latent Variable Models ◽  
Human Condition ◽  
Causal Relationships ◽  
Theoretical Perspectives ◽  
Psychometric Models ◽  
Psychometric Model ◽  
The Human Condition

Emotions are part and parcel of the human condition, but their nature is debated. Three broad classes of theories about the nature of emotions can be distinguished: affect-program theories, constructionist theories, and appraisal theories. Integrating these broad classes of theories into a unifying theory is challenging. An integrative psychometric model of emotions can inform such a theory because psychometric models are intertwined with theoretical perspectives about constructs. To identify an integrative psychometric model, we delineate properties of emotions stated by emotion theories and investigate whether psychometric models account for these properties. Specifically, an integrative psychometric model of emotions should allow (a) identifying distinct emotions (central in affect-program theories), (b) between- and within-person variations of emotions (central in constructionist theories), and (c) causal relationships between emotion components (central in appraisal theories). Evidence suggests that the popular reflective and formative latent variable models—in which emotions are conceptualized as unobservable causes or consequences of emotion components—cannot account for all properties. Conversely, a psychometric network model—in which emotions are conceptualized as systems of causally interacting emotion components—accounts for all properties. The psychometric network model thus constitutes an integrative psychometric model of emotions, facilitating progress toward a unifying theory.

scholarly journals A characterization of monotone unidimensional latent variable models

1997 ◽  
Vol 25 (3) ◽  
pp. 1327-1343 ◽  
Author(s):  
Jules L. Ellis ◽  
Brian W. Junker
Keyword(s):  
Latent Variable ◽  
Latent Variable Models

Right-Wing Authoritarians Aren't Very Funny: RWA, Personality, and Creative Humor Production

2020 ◽  
Author(s):  
Paul Silvia ◽  
Alexander P. Christensen ◽  
Katherine N. Cotter
Keyword(s):  
Young Adults ◽  
Latent Variable ◽  
Latent Variable Models ◽  
Openness To Experience ◽  
Rasch Models ◽  
Right Wing ◽  
Right Wing Authoritarianism ◽  
Humor Appreciation ◽  
Negative Effect

Right-wing authoritarianism (RWA) has well-known links with humor appreciation, such as enjoying jokes that target deviant groups, but less is known about RWA and creative humor production—coming up with funny ideas oneself. A sample of 186 young adults completed a measure of RWA, the HEXACO-100, and 3 humor production tasks that involved writing funny cartoon captions, creating humorous definitions for quirky concepts, and completing joke stems with punchlines. The humor responses were scored by 8 raters and analyzed with many-facet Rasch models. Latent variable models found that RWA had a large, significant effect on humor production (β = -.47 [-.65, -.30], p < .001): responses created by people high in RWA were rated as much less funny. RWA’s negative effect on humor was smaller but still significant (β = -.25 [-.49, -.01], p = .044) after controlling for Openness to Experience (β = .39 [.20, .59], p < .001) and Conscientiousness (β = -.21 [-.41, -.02], p = .029). Taken together, the findings suggest that people high in RWA just aren’t very funny.

Characterization of Soft-rot-resistant Amorphophallus konjac and Preliminary Analysis of the Resistance Mechanism

2013 ◽  
Vol 48 (3) ◽  
pp. 295-302
Author(s):  
Lei Zhenzhen ◽  
Ye Jinglong ◽  
Cheng Haili ◽  
Chen Yun ◽  
Wang Huixing ◽  
...  
Keyword(s):  
Preliminary Analysis ◽  
Resistance Mechanism ◽  
Soft Rot ◽  
Amorphophallus Konjac

scholarly journals Looking behind the choice of organic: A cross-country analysis applying Integrated Choice and Latent Variable Models

Appetite ◽  
2021 ◽  
pp. 105591
Author(s):  
Ching-Hua Yeh ◽  
Monika Hartmann ◽  
Matthew Gorton ◽  
Barbara Tocco ◽  
Virginie Amilien ◽  
...  
Keyword(s):  
Latent Variable ◽  
Latent Variable Models ◽  
Country Analysis ◽  
Cross Country

scholarly journals Gaussian Processes Proxy Model with Latent Variable Models and Variogram-Based Sensitivity Analysis for Assisted History Matching

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4290
Author(s):  
Dongmei Zhang ◽  
Yuyang Zhang ◽  
Bohou Jiang ◽  
Xinwei Jiang ◽  
Zhijiang Kang
Keyword(s):  
Sensitivity Analysis ◽  
Gaussian Processes ◽  
Latent Variable ◽  
History Matching ◽  
Latent Variable Models ◽  
High Dimensional ◽  
Model Parameters ◽  
Variable Model ◽  
Assisted History Matching ◽  
Proxy Models

Reservoir history matching is a well-known inverse problem for production prediction where enormous uncertain reservoir parameters of a reservoir numerical model are optimized by minimizing the misfit between the simulated and history production data. Gaussian Process (GP) has shown promising performance for assisted history matching due to the efficient nonparametric and nonlinear model with few model parameters to be tuned automatically. Recently introduced Gaussian Processes proxy models and Variogram Analysis of Response Surface-based sensitivity analysis (GP-VARS) uses forward and inverse Gaussian Processes (GP) based proxy models with the VARS-based sensitivity analysis to optimize the high-dimensional reservoir parameters. However, the inverse GP solution (GPIS) in GP-VARS are unsatisfactory especially for enormous reservoir parameters where the mapping from low-dimensional misfits to high-dimensional uncertain reservoir parameters could be poorly modeled by GP. To improve the performance of GP-VARS, in this paper we propose the Gaussian Processes proxy models with Latent Variable Models and VARS-based sensitivity analysis (GPLVM-VARS) where Gaussian Processes Latent Variable Model (GPLVM)-based inverse solution (GPLVMIS) instead of GP-based GPIS is provided with the inputs and outputs of GPIS reversed. The experimental results demonstrate the effectiveness of the proposed GPLVM-VARS in terms of accuracy and complexity. The source code of the proposed GPLVM-VARS is available at https://github.com/XinweiJiang/GPLVM-VARS.

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