[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120468-en":3,"doc-seo-120468-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120468,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Advancing Political Science With Machine Learning - A Gaussian Process Approach","The dissertation advances political science research by applying Gaussian process–based machine learning methods to multiple social-science problems. It develops a Gaussian-process item response framework for longitudinal survey analysis, including dynamic and multi-task formulations with Bayesian posterior inference. It further introduces an idiographic personality Gaussian process approach for psychological assessment, combining ordinal factor analysis and multi-task learning. Finally, it proposes a fully Bayesian treatment-effect estimation method for panel data, supported by posterior analysis and case studies, demonstrating improved modeling of uncertainty and temporal structure.","Washington University in St. Louis  \nWashU Scholarly Repository  \n\n| McKelvey School of Engineering Theses & Dissertations | McKelvey School of Engineering |\n| --- | --- |\n| 6-24-2025\u003Cbr>Advancing Political Science With Machine Learning: A Gaussian Process Approach\u003Cbr>Yehu Chen\u003Cbr>Washington University – McKelvey School of Engineering\u003Cbr>Follow this and additional works at: [https://openscholarship.wustl.edu/eng_etds](https://openscholarship.wustl.edu/eng_etds)\u003Cbr> Part of the Computer Sciences Commons |  |\n\nRecommended Citation  \nChen, Yehu, \"Advancing Political Science With Machine Learning: A Gaussian Process Approach\" (2025) . McKelvey School of Engineering Theses & Dissertations. 1223.  \n[https://openscholarship.wustl.edu/eng_etds/1223](https://openscholarship.wustl.edu/eng_etds/1223)  \nThis Dissertation is brought to you for free and open access by the McKelvey School of Engineering at WashU Scholarly Repository. It has been accepted for inclusion in McKelvey School of Engineering Theses & Dissertations by an authorized administrator of WashU Scholarly Repository. For more information, please contact [digital@wumail.wustl.edu](digital@wumail.wustl.edu).  \nWASHINGTON UNIVERSITY IN ST. LOUIS  \nMcKelvey School of Engineering  \nDivision of Computational & Data Sciences  \nDissertation Examination Committee:  \nRoman Garnett, Chair  \nJacob Montgomery, Co-Chair  \nSanmay Das  \nTed Enamorado  \nChien-Ju Ho  \nAdvancing Political Science With Machine Learning: A Gaussian Process Approach  \nby  \nYehu Chen  \nA dissertation presented to  \nthe McKelvey School of Engineering  \nof Washington University in  \npartial fulfillment of the  \nrequirements for the degree  \nof Doctor of Philosophy  \nAugust 2025  \nSt. Louis, Missouri  \n© 2025, Yehu Chen  \nTable of Contents  \nList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v  \nList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix  \nAcknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii  \nAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv  \nChapter 1: Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1  \n1. 1 Overview of Dissertation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3  \nChapter 2: Preliminary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6  \n2. 1 Gaussian Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6  \n2.2 Exact Inference with Gaussian Processes . . . . . . . . . . . . . . . . . . . . 8  \n2.3 Variational Inference for Gaussian Processes . . . . . . . . . . . . . . . . . . 9  \n2.4 Multi-Task Gaussian Processes . . . . . . . . . . . . . . . . . . . . . . . . . 11  \n2.5 Gaussian Processes for Social Science . . . . . . . . . . . . . . . . . . . . . . 12  \nChapter 3: Gaussian Process Item Response Theory for Longitudinal Survey 14  \n3.1 Item Response Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14  \n3.1. 1 Ordinal Item Response Theory . . . . . . . . . . . . . . . . . . . . . 15  \n3.1.2 Dynamic Item Response Theory . . . . . . . . . . . . . . . . . . . . . 16  \n3.2 Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17  \n3.2. 1 Generalized Dynamic Gaussian Process Item Response Theory . . . . 17  \n3.2.2 Posterior Inference with Markov chain Monte Carlo . . . . . . . . . . 19  \n3.3 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20  \n3.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22  \n3.4. 1 Evaluation with Simulated Data . . . . . . . . . . . . . . . . . . . . . 22  \n3.4.2 Case Study: Public Confidence on Economy .............. 24  \n3.4.3 Case Study: Ideology of U.S. Senate on Abortion ........... 26  \n3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29  \nChapter 4: Idiographic Personality","cbCainHPr7rq1mrb","https://ap.wps.com/l/cbCainHPr7rq1mrb","pdf",2871467,1,165,"English","en",105,"# Table of Contents\n## List of Figures\n## List of Tables\n## Acknowledgments\n## Abstract\n## Chapter 1: Introduction\n## Chapter 2: Preliminary\n## Chapter 3: Gaussian Process Item Response Theory for Longitudinal Survey\n## Chapter 4: Idiographic Personality Framework for Psychological Assessment\n## Chapter 5: Treatment Effect Estimation in Panel Data","[{\"question\":\"What is the main methodological focus of the dissertation?\",\"answer\":\"The dissertation centers on Gaussian process–based machine learning methods to model uncertainty and temporal structure in social-science settings.\"},{\"question\":\"How does it address longitudinal survey data?\",\"answer\":\"It proposes a Gaussian process item response theory framework for longitudinal surveys, including dynamic ordinal formulations and Bayesian posterior inference with MCMC.\"},{\"question\":\"What problem does the dissertation tackle in panel data?\",\"answer\":\"It estimates treatment effects in panel data using a fully Bayesian approach, including a multi-task Gaussian process model and posterior analysis supported by experiments and case studies.\"}]","Advancing Political Science With Machine Learning - 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