Topics in Markov chain Monte Carlo methods, with applications in statistics
Abstract/Contents
- Abstract
- Markov chain Monte Carlo (MCMC) methods, such as the Metropolis-Hastings algorithms, the Gibbs sampler, are ubiquitous in almost every quantitative subject of study such as physics, chemistry, statistics, biology, and computer science. In this thesis, we focus on the following two kinds of sampling problems: 1. How to efficiently sample binary matrices with fixed row and column totals uniformly at random? 2. How to draw samples from 'doubly intractable' distributions? For both of the two problems, we explore the theoretical properties of the existing MCMC algorithms and develop new methods to improve the existing algorithms
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2020; ©2020 |
| Publication date | 2020; 2020 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Wang, Guanyang |
|---|---|
| Degree supervisor | Diaconis, Persi |
| Thesis advisor | Diaconis, Persi |
| Thesis advisor | Palacios Roman, Julia Adela |
| Thesis advisor | Wong, Wing Hung |
| Degree committee member | Palacios Roman, Julia Adela |
| Degree committee member | Wong, Wing Hung |
| Associated with | Stanford University, Department of Mathematics |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Guanyang Wang |
|---|---|
| Note | Submitted to the Department of Mathematics |
| Thesis | Thesis Ph.D. Stanford University 2020 |
| Location | electronic resource |
Access conditions
- Copyright
- © 2020 by Guanyang Wang
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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