Hierarchical two-part MDL code for multinomial distributions
| Autor: | Marc Boullé |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
Applied Mathematics Model selection 02 engineering and technology Density estimation Parameter space Normalized maximum likelihood Theoretical Computer Science ComputingMethodologies_PATTERNRECOGNITION Artificial Intelligence 020204 information systems 0202 electrical engineering electronic engineering information engineering Leverage (statistics) 020201 artificial intelligence & image processing Multinomial distribution Minimum description length Algorithm Software |
| Zdroj: | International Journal of Approximate Reasoning. 103:71-93 |
| ISSN: | 0888-613X |
| DOI: | 10.1016/j.ijar.2018.09.002 |
| Popis: | We leverage the Minimum Description Length (MDL) principle as a model selection technique for multinomial distributions and suggest a two-part MDL code based on a hierarchical encoding of the multinomial parameters. We compare this code with the alternative Normalized Maximum Likelihood (NML) code and exhibit large regions of the parameter space where the hierarchical code dominates the NML one. We then present an application of the multinomial distribution to joint density estimation and show that the hierarchical code brings significant improvements. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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