Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Institution
-
- Utah State University (5)
- University of Wisconsin Milwaukee (4)
- University of Kentucky (3)
- Claremont Colleges (2)
- Georgia Southern University (2)
-
- Harrisburg University of Science and Technology (2)
- University of Texas at El Paso (2)
- Brigham Young University (1)
- East Tennessee State University (1)
- Embry-Riddle Aeronautical University (1)
- Illinois State University (1)
- The College of Wooster (1)
- The Texas Medical Center Library (1)
- University of Montana (1)
- University of New Mexico (1)
- University of South Carolina (1)
- University of Vermont (1)
- Washington University in St. Louis (1)
- Publication Year
- Publication
-
- Theses and Dissertations (6)
- All Graduate Theses and Dissertations, Spring 1920 to Summer 2023 (4)
- Electronic Theses and Dissertations (3)
- Theses and Dissertations--Mathematics (3)
- Dissertations and Theses (2)
-
- Open Access Theses & Dissertations (2)
- All Graduate Plan B and other Reports, Spring 1920 to Spring 2023 (1)
- Arts & Sciences Electronic Theses and Dissertations (1)
- CGU Theses & Dissertations (1)
- CMC Senior Theses (1)
- Dissertations & Theses (Open Access) (1)
- Doctoral Dissertations and Master's Theses (1)
- Graduate College Dissertations and Theses (1)
- Graduate Student Theses, Dissertations, & Professional Papers (1)
- Mathematics & Statistics ETDs (1)
- Senior Independent Study Theses (1)
- Senior Theses (1)
Articles 31 - 31 of 31
Full-Text Articles in Physical Sciences and Mathematics
A Topics Analysis Model For Health Insurance Claims, Jared Anthony Webb
A Topics Analysis Model For Health Insurance Claims, Jared Anthony Webb
Theses and Dissertations
Mathematical probability has a rich theory and powerful applications. Of particular note is the Markov chain Monte Carlo (MCMC) method for sampling from high dimensional distributions that may not admit a naive analysis. We develop the theory of the MCMC method from first principles and prove its relevance. We also define a Bayesian hierarchical model for generating data. By understanding how data are generated we may infer hidden structure about these models. We use a specific MCMC method called a Gibbs' sampler to discover topic distributions in a hierarchical Bayesian model called Topics Over Time. We propose an innovative use …