Bayesian structural learning in decision analysis
Abstract/Contents
- Abstract
- This work presents a method of using data to facilitate the creation of influence diagrams as part of model generation. The problem that motivates this dissertation is a bottom-up modeling approach of Decision Analysis that uses expert interviews to define variables, distinctions and capture the relations between the variables that reflect the current state of information about the decision situation. Creation and parameterization of the model defined in such way is a lengthy and error-prone process that poses a significant challenge in real-life applications. The proposed method of using Bayesian Structural Learning assumes the presence of some prior data on the problem and bootstraps relevance diagrams from such dataset. This novel approach assumes a structured coordination between machine learning methods and human experts, and in return offers extended opportunities in model generation while avoiding the common pitfalls stemming from the difficulties of eliciting conditional probabilities. We demonstrate the workings of this method using synthetic and real-life examples, and describe how to take full advantage of artificial intelligence algorithms -- including unsupervised learning with neural networks -- to facilitate work in the decision modeling phase.
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 | Kharitonov, Daniel |
|---|---|
| Degree supervisor | Howard, Ronald A. (Ronald Arthur), 1934- |
| Degree supervisor | Tse, Edison |
| Thesis advisor | Howard, Ronald A. (Ronald Arthur), 1934- |
| Thesis advisor | Tse, Edison |
| Thesis advisor | Shachter, Ross D |
| Degree committee member | Shachter, Ross D |
| Associated with | Stanford University, Department of Management Science and Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Daniel Kharitonov. |
|---|---|
| Note | Submitted to the Department of Management Science and Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2020. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2020 by Daniel Kharitonov
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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