Pólya‐gamma data augmentation and latent variable models for multivariate binomial data

2021 ◽  
Author(s):  
John B. Holmes ◽  
Matthew R. Schofield ◽  
Richard J. Barker
Keyword(s):  
Latent Variable ◽  
Data Augmentation ◽  
Latent Variable Models ◽  
Binomial Data

scholarly journals Data Augmentation for Spoken Language Understanding via Joint Variational Generation

2019 ◽  
Vol 33 ◽  
pp. 7402-7409 ◽  
Author(s):  
Kang Min Yoo ◽  
Youhyun Shin ◽  
Sang-goo Lee
Keyword(s):  
Latent Variable ◽  
Data Augmentation ◽  
Domain Adaptation ◽  
Latent Variable Models ◽  
Spoken Language ◽  
Generative Models ◽  
Statistical Testing ◽  
Language Understanding ◽  
Data Scarcity ◽  
Spoken Language Understanding

Data scarcity is one of the main obstacles of domain adaptation in spoken language understanding (SLU) due to the high cost of creating manually tagged SLU datasets. Recent works in neural text generative models, particularly latent variable models such as variational autoencoder (VAE), have shown promising results in regards to generating plausible and natural sentences. In this paper, we propose a novel generative architecture which leverages the generative power of latent variable models to jointly synthesize fully annotated utterances. Our experiments show that existing SLU models trained on the additional synthetic examples achieve performance gains. Our approach not only helps alleviate the data scarcity issue in the SLU task for many datasets but also indiscriminately improves language understanding performances for various SLU models, supported by extensive experiments and rigorous statistical testing.

Discrete Latent Variable Models

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Francesco Bartolucci ◽  
Silvia Pandolfi ◽  
Fulvia Pennoni
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Latent Class ◽  
Data Augmentation ◽  
Expectation Maximization Algorithm ◽  
Latent Variable Models ◽  
Annual Review ◽  
Publication Date ◽  
Variable Approach ◽  
Block Models

We review the discrete latent variable approach, which is very popular in statistics and related fields. It allows us to formulate interpretable and flexible models that can be used to analyze complex datasets in the presence of articulated dependence structures among variables. Specific models including discrete latent variables are illustrated, such as finite mixture, latent class, hidden Markov, and stochastic block models. Algorithms for maximum likelihood and Bayesian estimation of these models are reviewed, focusing, in particular, on the expectation–maximization algorithm and the Markov chain Monte Carlo method with data augmentation. Model selection, particularly concerning the number of support points of the latent distribution, is discussed. The approach is illustrated by summarizing applications available in the literature; a brief review of the main software packages to handle discrete latent variable models is also provided. Finally, some possible developments in this literature are suggested. Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 9 is March 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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.

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.

scholarly journals Longitudinal Analysis of Patient-Reported Outcomes in Clinical Trials: Applications of Multilevel and Multidimensional Item Response Theory

Psychometrika ◽  
2021 ◽  
Author(s):  
Li Cai ◽  
Carrie R. Houts
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Latent Variable ◽  
Patient Reported Outcomes ◽  
Multilevel Models ◽  
Latent Variable Models ◽  
Trial Data ◽  
Regulatory Science ◽  
Measurement Information ◽  
Patient Reported

AbstractWith decades of advance research and recent developments in the drug and medical device regulatory approval process, patient-reported outcomes (PROs) are becoming increasingly important in clinical trials. While clinical trial analyses typically treat scores from PROs as observed variables, the potential to use latent variable models when analyzing patient responses in clinical trial data presents novel opportunities for both psychometrics and regulatory science. An accessible overview of analyses commonly used to analyze longitudinal trial data and statistical models familiar in both psychometrics and biometrics, such as growth models, multilevel models, and latent variable models, is provided to call attention to connections and common themes among these models that have found use across many research areas. Additionally, examples using empirical data from a randomized clinical trial provide concrete demonstrations of the implementation of these models. The increasing availability of high-quality, psychometrically rigorous assessment instruments in clinical trials, of which the Patient-Reported Outcomes Measurement Information System (PROMIS®) is a prominent example, provides rare possibilities for psychometrics to help improve the statistical tools used in regulatory science.

scholarly journals Latent Variable Models and Networks: Statistical Equivalence and Testability

2019 ◽  
pp. 1-24 ◽  
Author(s):  
Riet van Bork ◽  
Mijke Rhemtulla ◽  
Lourens J. Waldorp ◽  
Joost Kruis ◽  
Shirin Rezvanifar ◽  
...  
Keyword(s):  
Latent Variable ◽  
Latent Variable Models ◽  
Statistical Equivalence
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