ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1301.6465
53
8
v1v2 (latest)

Extendable MDL

28 January 2013
P. Harremoes
ArXiv (abs)PDFHTML
Abstract

In this paper we show that combination of the minimum description length principle and a exchange-ability condition leads directly to the use of Jeffreys prior. This approach works in most cases even when Jeffreys prior cannot be normalized. Kraft's inequality links codes and distributions but a closer look at this inequality demonstrates that this link only makes sense when sequences are considered as prefixes of potential longer sequences. For technical reasons only results for exponential families are stated. Results on when Jeffreys prior can be normalized after conditioning on a initializing string are given. An exotic case where no initial string allow Jeffreys prior to be normalized is given and some way of handling such exotic cases are discussed.

View on arXiv
Comments on this paper