[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123981-en":3,"doc-seo-123981-105":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":4,"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},123981,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Model-Driven Cosmology With Bayesian Machine Learning and Population Inference","This thesis presents new directions for cosmological data analysis using Bayesian techniques and machine learning. The research focuses on model-driven inference, combining probabilistic reasoning with computational learning to improve how populations are characterized from observational data. By translating complex physics questions into rigorous statistical workflows, the work aims to enhance interpretability, quantify uncertainties, and strengthen the connection between theoretical modeling and empirical constraints across cosmological applications.","UC Riverside  \nUC Riverside Electronic Theses and Dissertations  \nTitle  \nModel-Driven Cosmology With Bayesian Machine Learning and Population Inference  \nPermalink  \n[https://escholarship.org/uc/item/55x0s0dr](https://escholarship.org/uc/item/55x0s0dr)  \nAuthor  \nHo, Ming-Feng  \nPublication Date  \n2024  \nSupplemental Material  \n[https://escholarship.org/uc/item/55x0s0dr\\#supplemental](https://escholarship.org/uc/item/55x0s0dr#supplemental)  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons Attribution License, available at [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \n[Peer reviewed|Thesis/dissertation](Peer reviewed|Thesis/dissertation)  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA  \nRIVERSIDE  \nModel-Driven Cosmology With Bayesian Machine Learning and Population  \nInference  \nA Dissertation submitted in partial satisfaction  \nof the requirements for the degree of  \nDoctor of Philosophy  \nin  \nPhysics  \nby  \nMing-Feng Ho  \nSeptember 2024  \nDissertation Committee:  \nDr. Simeon Bird, Chairperson  \nDr. Hai-bo Yu  \nDr. Anson D’Aloisio  \nCopyright by Ming-Feng Ho 2024  \nThe Dissertation of Ming-Feng Ho is approved:  \nCommittee Chairperson  \nUniversity of California, Riverside  \nAcknowledgments  \nFirst and foremost, I would like to thank my advisor, Simeon Bird. Simeon gave me the freedom to focus solely on Bayesian statistics and machine learning throughout my graduate career. This allowed me to develop a variety of mathematical and computational skills that have become my greatest assets. Simeon also supported me both personally and academically. He is an excellent performer, and I learned a great deal from him on how to translate complex physics problems into humorous, everyday life examples.  \nNext, I am grateful to my family for their unconditional support. My parents have always been there for me, and my brother has been a great friend, offering valuable advice on how to improve my academic journey. My extended family, including my grandparents and aunts, have also been incredibly supportive. Also, my cousins have been my lifelong friends and have provided me supports. I am especially thankful to my partner, who has provided me with so many mental supports. I am grateful for her presence in my life.  \nAlso, I would like to thank my old friends in the PG study group in National Taiwan University: Li-Hong, Po-Ya, Sheng-Chang, Tai-Yin, and Yu-Chung. They have supported me since the beginning of both my academic and non-academic journeys. Li-Hong has been a considerate friend, providing me with numerous intriguing ideas regarding cybersecurity. Po-Ya has been an excellent social leader for the group, helping to bring everyone together. Sheng-Chang has exemplified what a serious theoretical physicist looks like in the modern era. Tai-Yin has shared his insights on the fascinating aspects of machine learning. YuChung has been a sincere friend, showcasing his passion for physics and research.  \nNext, I would like to thank my committee members, Hai-Bo Yu, Anson D’Aloisio, and George Becker and Christian Shelton (for PhD candidacy exam), who have been extremely helpful and supportive. I am also grateful to my friends in Bird’s group: Phoebe has been very helpful in teaching me how to be a good research mentor to undergrads/highschool students. Bryan has thoughtfully helped me understand the astronomy culture in California. Martin has been invaluable in helping me become a better collaborator. Reza has been very supportive and guided me through graduate school as both close friend and colleague . Mahdi (Sum) has been a great comrade throughout our graduate studies. Hurum has been a good officemate to chat with, and Yanhui has been an excellent colleague with excellent programming skills. I am also grateful to my collaborator, Scott, who has been very supporti","cbCainK1BajBNbLZ","https://ap.wps.com/l/cbCainK1BajBNbLZ","pdf",26896831,1,391,"English","en",105,"# Acknowledgments\n# ABSTRACT OF THE DISSERTATION","[{\"question\":\"What is the core focus of the dissertation?\",\"answer\":\"The dissertation focuses on cosmological data analysis using Bayesian techniques and machine learning, emphasizing model-driven inference and population characterization.\"},{\"question\":\"How does the work approach uncertainty?\",\"answer\":\"It uses Bayesian methods to quantify uncertainty within statistical inference, integrating probabilistic reasoning with computational learning workflows.\"},{\"question\":\"What is the academic context of the dissertation?\",\"answer\":\"It is submitted for the degree of Doctor of Philosophy in Physics at the University of California, Riverside, dated September 2024.\"}]","Model-Driven Cosmology With Bayesian Machine Learning and Population Inference | PDF",1785819595,985,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"model-driven-cosmology-with-bayesian-machine-learning-and-population-inference","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/model-driven-cosmology-with-bayesian-machine-learning-and-population-inference/123981/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the core focus of the dissertation?","Question",{"text":75,"@type":76},"The dissertation focuses on cosmological data analysis using Bayesian techniques and machine learning, emphasizing model-driven inference and population characterization.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the work approach uncertainty?",{"text":80,"@type":76},"It uses Bayesian methods to quantify uncertainty within statistical inference, integrating probabilistic reasoning with computational learning workflows.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the academic context of the dissertation?",{"text":84,"@type":76},"It is submitted for the degree of Doctor of Philosophy in Physics at the University of California, Riverside, dated September 2024.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]