Pith. sign in

REVIEW 3 cited by

A tutorial introduction to the minimum description length principle

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv math/0406077 v1 pith:ER2JIO26 submitted 2004-06-04 math.ST cs.ITcs.LGmath.ITstat.TH

A tutorial introduction to the minimum description length principle

classification math.ST cs.ITcs.LGmath.ITstat.TH
keywords introductionchapterdescriptionfirstlengthminimumtutorialconceptual
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

This tutorial provides an overview of and introduction to Rissanen's Minimum Description Length (MDL) Principle. The first chapter provides a conceptual, entirely non-technical introduction to the subject. It serves as a basis for the technical introduction given in the second chapter, in which all the ideas of the first chapter are made mathematically precise. The main ideas are discussed in great conceptual and technical detail. This tutorial is an extended version of the first two chapters of the collection "Advances in Minimum Description Length: Theory and Application" (edited by P.Grunwald, I.J. Myung and M. Pitt, to be published by the MIT Press, Spring 2005).

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Characterizing Learning in Deep Neural Networks using Tractable Algorithmic Complexity Analysis

    cs.LG 2026-05 unverdicted novelty 7.0

    QuBD extends algorithmic complexity estimation to quantized DNN weights, revealing that complexity decreases during learning, increases with overfitting, follows grokking patterns, and correlates with generalization.

  2. LongSeeker: Elastic Context Orchestration for Long-Horizon Search Agents

    cs.AI 2026-05 unverdicted novelty 6.0

    Context-ReAct enables agents to dynamically manage context via five atomic operations, and LongSeeker fine-tuned on 10k trajectories achieves 61.5% and 62.5% on BrowseComp benchmarks, outperforming prior agents.

  3. Inferring Latent dimension of Linear Dynamical System with Minimum Description Length

    cs.LG 2019-06 unverdicted novelty 4.0

    Proposes an MDL-based criterion for selecting the latent dimension of linear dynamical systems that accounts for latent structure omitted in prior work.