A probabilistic graphical models approach to model interconnectedness
| Autor: | Alexander Denev, Adrien Papaioannou, Orazio Angelini |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry Context (language use) Management Science and Operations Research computer.software_genre Stress testing (software) Machine learning Interconnectedness Expert system Task (project management) Multiple Models Graphical model Artificial intelligence Statistics Probability and Uncertainty Business and International Management business computer |
| Zdroj: | International Journal of Risk Assessment and Management. 23:119 |
| ISSN: | 1741-5241 1466-8297 |
| Popis: | In this paper, we show that using multiple models when executing a specific task almost unavoidably gives rise to interaction between them, especially when their number is large. We show that this interaction can lead to biased and incomplete results if treated inappropriately (which we believe is the current standard in the financial industry). We propose the use of probabilistic graphical models – a technique widely used in machine learning and expert systems as a remedy to this problem. We discuss some numerical aspects of our approach that will be present in any practical implementation. We then examine, in detail, a practical example of using this method in a stress testing context. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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