Efficient simulation for complex systems
Abstract/Contents
- Abstract
- This dissertation consists of two parts, with the central topic on efficient simulation. The first part focuses on the problem of optimal stratified sampling with finitely many and infinitely many strata. We present a more general framework for analysis and design of adaptive sampling algorithms achieving optimal convergence rate. We show that asymptotic optimal variance can be achieved. The second part focuses on hospital-level COVID demand forecast, where we provide a method to generate consistent forecast intervals. We show that no stationarity assumption of the underlying point process is required for the proposed method. Furthermore, the model is computationally lightweight to estimate, as compared to epidemiology and machine learning based models.
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 | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Zhang, Teng |
|---|---|
| Degree supervisor | Blanchet, Jose H |
| Degree supervisor | Glynn, Peter W |
| Thesis advisor | Blanchet, Jose H |
| Thesis advisor | Glynn, Peter W |
| Thesis advisor | Bambos, Nicholas |
| Thesis advisor | Scheinker, David |
| Degree committee member | Bambos, Nicholas |
| Degree committee member | Scheinker, David |
| Associated with | Stanford University, Department of Management Science and Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Teng Zhang. |
|---|---|
| Note | Submitted to the Department of Management Science and Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/kt775dj4761 |
Access conditions
- Copyright
- © 2021 by Teng Zhang
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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