Generalizing the number of states in Bayesian belief propagation, as applied to portfolio management.
| dc.contributor.author | Kruger, Jan Walters. | |
| dc.date.accessioned | 2018-12-18T12:12:41Z | |
| dc.date.available | 2018-12-18T12:12:41Z | |
| dc.date.issued | 1996 | |
| dc.description | A research report submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in partial fulfillment of the requirements for the degree of Master of' Science. | en_ZA |
| dc.description.abstract | This research report describes the use or the Pearl's algorithm in Bayesian belief networks to induce a belief network from a database. With a solid grounding in probability theory, the Pearl algorithm allows belief updating by propagating likelihoods of leaf nodes (variables) and the prior probabilities. The Pearl algorithm was originally developed for binary variables and a generalization to more states is investigated. The data 'Used to test this new method, in a Portfolio Management context, are the Return and various attributes of companies listed on the Johannesburg Stock Exchange ( JSE ). The results of this model is then compared to a linear regression model. The bayesian method is found to perform better than a linear regression approach. | en_ZA |
| dc.description.librarian | Andrew Chakane 2018 | en_ZA |
| dc.identifier.uri | https://hdl.handle.net/10539/26225 | |
| dc.language.iso | en | en_ZA |
| dc.subject | BAYESIAN STATISTICAL DECISION THEORY. | en_ZA |
| dc.subject | PROBABILITIES. | en_ZA |
| dc.subject | PORTFOLIO MANAGEMENT--MATHEMATICAL MODELS. | en_ZA |
| dc.subject | ALGORITHMS. | en_ZA |
| dc.title | Generalizing the number of states in Bayesian belief propagation, as applied to portfolio management. | en_ZA |
| dc.type | Thesis | en_ZA |
Files
Original bundle
1 - 1 of 1
No Thumbnail Available
- Name:
- Kruger Jan Walters._Generalizing the number of sta.pdf
- Size:
- 1.88 MB
- Format:
- Adobe Portable Document Format
- Description:
License bundle
1 - 1 of 1
No Thumbnail Available
- Name:
- license.txt
- Size:
- 1.71 KB
- Format:
- Item-specific license agreed upon to submission
- Description: