Algorithmic information theory
| dc.creator | Grunwald, Peter D. | |
| dc.creator | Vitanyi, Paul M. B. | |
| dc.date | 2008-09-16 | |
| dc.date | 2008-09-17 | |
| dc.date.accessioned | 2026-07-07T10:03:18Z | |
| dc.date.available | 2026-07-07T10:03:18Z | |
| dc.description | We introduce algorithmic information theory, also known as the theory of Kolmogorov complexity. We explain the main concepts of this quantitative approach to defining `information'. We discuss the extent to which Kolmogorov's and Shannon's information theory have a common purpose, and where they are fundamentally different. We indicate how recent developments within the theory allow one to formally distinguish between `structural' (meaningful) and `random' information as measured by the Kolmogorov structure function, which leads to a mathematical formalization of Occam's razor in inductive inference. We end by discussing some of the philosophical implications of the theory. | |
| dc.description | 37 pages, 2 figures, pdf, in: Philosophy of Information, P. Adriaans and J. van Benthem, Eds., A volume in Handbook of the philosophy of science, D. Gabbay, P. Thagard, and J. Woods, Eds., Elsevier, 2008. In version 1 of September 16 the refs are missing. Corrected in version 2 of September 17 | |
| dc.identifier | https://arxiv.org/abs/0809.2754 | |
| dc.identifier | http://arxiv.org/abs/0809.2754 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/169239 | |
| dc.subject | Information Theory | |
| dc.subject | Machine Learning | |
| dc.subject | Statistics Theory | |
| dc.title | Algorithmic information theory | |
| dc.type | text |