A Dilemma for Solomonoff Prediction.
The framework of Solomonoff prediction assigns prior probability to hypotheses inversely proportional to their Kolmogorov complexity. There are two well-known problems. First, the Solomonoff prior is relative to a choice of universal Turing machine. Second, the Solomonoff prior is not computable. Ho...
| Publicado en: | Philosophy of Science Vol. 90; no. 2; pp. 288 - 307 |
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| Autor principal: | |
| Formato: | Artículo |
| Publicado: |
Cambridge University Press
Apr2023
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| Sumario: | The framework of Solomonoff prediction assigns prior probability to hypotheses inversely proportional to their Kolmogorov complexity. There are two well-known problems. First, the Solomonoff prior is relative to a choice of universal Turing machine. Second, the Solomonoff prior is not computable. However, there are responses to both problems. Different Solomonoff priors converge with more and more data. Further, there are computable approximations to the Solomonoff prior. I argue that there is a tension between these two responses. This is because computable approximations to Solomonoff prediction do not always converge. |
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