Cultural advice

The Australian National University acknowledges, celebrates and pays our respects to the Ngunnawal and Ngambri people of the Canberra region and to all First Nations Australians on whose traditional lands we meet and work, and whose cultures are among the oldest continuing cultures in human history.

Aboriginal and Torres Strait Islander peoples are advised that ANU Library collections may include images, names, voices, and other representations of deceased persons.

Material in the collection may contain terms, language or views that reflect the period in which the item was created and may be considered inappropriate today.

Algorithmic probability

Thumbnail Image

Date

2007

Authors

Vitanyi, Paul
Legg, Shane
Hutter, Marcus

Journal Title

Journal ISSN

Volume Title

Publisher

Scholarpedia

Abstract

Algorithmic "Solomonoff" Probability (AP) assigns to objects an a priori probability that is in some sense universal. This prior distribution has theoretical applications in a number of areas, including inductive inference theory and the time complexity analysis of algorithms. Its main drawback is that it is not computable and thus can only be approximated in practice.

Description

Keywords

algorithmic information theory, algorithmic complexity, discrete/continuous algorithmic probability

Citation

URI

http://hdl.handle.net/1885/15013

Collections

ANU Research Publications

Source

Scholarpedia

Type

Journal article

Book Title

Entity type

Access Statement

License Rights

DOI

10.4249/scholarpedia.2572

Restricted until

Downloads

File
Description
Hutter et al Algorithmic Probability 2007.pdf (437.21 KB)

DSpace software copyright © 2002-2026 LYRASIS

abcd