The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Complexity of strings in the class of Markov sources
Abstract
Shannon's self-information of a string is generalized to its complexity relative to the class of finite-state-machine (FSM) defined sources. Unlike an earlier generalization, the new one is valid for both short and long strings. The definition is justified in part by a theorem stating that, asymptotically, the mean complexity provides a tight lower bound for the mean length of all so-called regular codes. This also generalizes Shannon's noiseless coding theorem. For a large subclass of FSM sources a simple algorithm is described for computing the complexity.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A formal theory of inductive inference. Part I
Solomonoff, R. J. (1964) · Information and Control · doi:10.1016/s0019-9958(64)90223-2 · Cited by 1,192
On the Length of Programs for Computing Finite Binary Sequences
Chaitin, G. J. (1966) · Journal of the ACM · doi:10.1145/321356.321363 · Cited by 913
Cited by
Prediction, Learning, and Games
Cesa-Bianchi, N., & Lugosi, G. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511546921 · Cited by 3,162
The hierarchical hidden Markov model: Analysis and applications
Fine, S., Singer, Y., & Tishby, N. (1998) · Machine Learning · doi:10.1023/a:1007469218079 · Cited by 906
The power of amnesia: Learning probabilistic automata with variable memory length
Ron, D., Singer, Y., & Tishby, N. (1997) · Machine Learning · doi:10.1007/bf00114008 · Cited by 447
Merhav, N., & Feder, M. (1998) · IEEE Transactions on Information Theory · doi:10.1109/18.720534 · Cited by 438
Universal prediction of individual sequences
Feder, M., Merhav, N., & Gutman, M. (1992) · IEEE Transactions on Information Theory · doi:10.1109/18.144706 · Cited by 373
Metadata
- Authors
- Rissanen, J.
- Venue
- IEEE Transactions on Information Theory
- Published
- 1986-07
- Volume
- 32
- Issue
- 4
- Pages
- 526-532
- ISSN
- 0018-9448 (ISSN-L)
- 1557-9654
- OpenAlex
- W2148381206
- Cited by
- 192
- References
- 2
- FWCI
- 2.42
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Complexity of strings in the class of Markov sources. https://app.cochat.ai/papers/10.1109/tit.1986.1057210
- The paper
- Rissanen, J. (1986). Complexity of strings in the class of Markov sources. IEEE Transactions on Information Theory. https://doi.org/10.1109/tit.1986.1057210
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
