The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
A formal theory of inductive inference. Part II
Open accessOverview
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“The ability to generalise is closely related to compression [14,15], with more compressed representations yielding better accuracy [13] for the same reason that there is only one straight line interpolating any two points, but infinitely many polynomials of higher degree.”
From Compression, The Fermi Paradox and Artificial Super-Intelligence (2022) · cites this paper as [15]
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
On computable numbers, with an application to the entscheidungsproblem
Turing, A. M. (1937) · Proceedings of the London Mathematical Society · doi:10.1112/plms/s2-42.1.230 · Cited by 8,283
Banks, D. L., Ríos, J., & Insua, D. R. (2015) · Chapman & Hall/CRC eBooks · doi:10.1201/b18653-3 · Cited by 3,649
Three models for the description of language
Chomsky, N. (1956) · IEEE Transactions on Information Theory · doi:10.1109/tit.1956.1056813 · Cited by 2,984
Keynes, J. M. (1973) · Palgrave Macmillan UK eBooks · doi:10.1007/978-1-349-00843-8 · Cited by 2,576
Logical Foundations of Probability
Savage, L. J., & Carnap, R. (1952) · Econometrica · doi:10.2307/1907654 · Cited by 2,042
Computer Languages for Symbol Manipulation
Green, B. F. (1961) · IRE Transactions on Electronic Computers · doi:10.1109/tec.1961.5219281 · Cited by 11
Cited by
Sorted by citations
Items 901–1,000 of 1,001
WILLIAM: A Monolithic Approach to AGI
Franz, A., Gogulya, V., & Löffler, M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-27005-6_5 · Cited by 7
Toward a Simulation Model Complexity Measure
Thompson, J. S., Hodson, D. D., Grimaila, M. R., Hanlon, N., & Dill, R. (2023) · Information · doi:10.3390/info14040202 · Cited by 7
Lloyd, S., & Dreyer, O. (2015) · Quantum Information Processing · doi:10.1007/s11128-015-1178-7 · Cited by 7
Occam and Bayes in predicting category intuitiveness
Pothos, E. M. (2007) · Artificial Intelligence Review · doi:10.1007/s10462-009-9102-0 · Cited by 7
Zenil, H., Kiani, N. A., & Tegner, J. N. (2018) · Entropy · doi:10.3390/e20070534 · Cited by 7
Prediction Games in Infinitely Rich Worlds
Madani, O. (2007) · Cited by 7
Uncomputability and Physical Law
Lloyd, S. (2017) · Theory and applications of computability · doi:10.1007/978-3-319-43669-2_6 · Cited by 7
Concepts and Autonomous Agents
Davidsson, P., Lu–Cs–Tr, & Balkenius, C. (1997) · Cited by 7
Reinforcement learning for procurement agents of factory of the future
Simsek, B., Albayrak, Ş., & Korth, A. (2005) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2004.1331051 · Cited by 7
Verkerk, R. H. J., & Hickey, S. (2009) · Toxicology · doi:10.1016/j.tox.2009.12.017 · Cited by 7
Inductive learning by machines
Russell, S. (1991) · Philosophical Studies · doi:10.1007/bf00356089 · Cited by 7
On the computational power of random strings
Day, A. R. (2009) · Annals of Pure and Applied Logic · doi:10.1016/j.apal.2009.03.001 · Cited by 7
Pseudorandom Generators: A Primer
Goldreich, O. (2008) · Cited by 7
Optimal asymptotic bounds on the oracle use in computations from Chaitin's Omega
Barmpalias, G., Fang, N., & Lewis-Pye, A. (2016) · Journal of Computer and System Sciences · doi:10.1016/j.jcss.2016.05.004 · Cited by 7
Confirmation bias is rational when hypotheses are sparse
Perfors, A., & Navarro, D. (2009) · eScholarship (California Digital Library) · Cited by 7
Low-Depth Witnesses are Easy to Find
Antunes, L. F., Fortnow, L., Pinto, A., & Souto, A. (2007) · Computational Complexity · doi:10.1007/s00037-011-0025-1 · Cited by 7
Automatic Acquisition of Language Models for Speech Recognition
McCandless, M. K. (1994) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 7
Multisensor fusion and unknown statistics
Joshi, R., & Sanderson, A. C. (2002) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1995.525660 · Cited by 7
A minimum code length technique for clustering of syntactic patterns
Fred, A. L. N., & Leitão, J. M. N. (1996) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.1996.546909 · Cited by 7
A theorem about automatic programming
Kugel, P. (1975) · ACM SIGART Bulletin · doi:10.1145/1045231.1045233 · Cited by 7
Quantum field theory and algorithmic complexity
Woo, C. H. (1986) · Physics Letters B · doi:10.1016/0370-2693(86)91647-3 · Cited by 7
Solomonoff coding as a means of introducing prior information in syntactic pattern recognition
Fred, A. L. N., & Leitão, J. M. N. (2002) · IAPR International Conference on Pattern Recognition (ICPR) · doi:10.1109/icpr.1994.576867 · Cited by 7
Inductive Inference by Using Information Compression
Choi, B. (2003) · Computational Intelligence · doi:10.1111/1467-8640.00218 · Cited by 7
Savage, J. E. (1975) · Springer eBooks · doi:10.1007/978-3-7091-3008-7_6 · Cited by 7
Signal Recovery in Compressed Sensing via Universal PriorsPreprint
Baron, D., & Duarte, M. F. (2012) · arXiv (Cornell University) · Cited by 7
Stream Ciphers and Linear Complexity
Qi, H. (2007) · National University of Singapore · Cited by 7
Solutions to problems with deep learningPreprint
Wolff, J. G. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.05457 · Cited by 7
�ber die Definition von effektiven Zufallstests
Schnorr, C. P. (1970) · Probability Theory and Related Fields · doi:10.1007/bf00533301 · Cited by 7
The Optimal Choice of Hypothesis Is the Weakest, Not the ShortestPreprint
Bennett, M. T. (2023) · doi:10.36227/techrxiv.21965675.v2 · Cited by 7
CAEMSI : A Cross-Domain Analytic Evaluation Methodology for Style Imitation.
Ens, J., & Pasquier, P. (2018) · ICCC · Cited by 6
An Optimal Superfarthingale and Its Convergence over a Computable Topological Space
Miyabe, K. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-44958-1_21 · Cited by 6
One-Way Functions Using Algorithmic and Classical Information Theories
Antunes, L. F., Matos, A. B., Pinto, A., Souto, A., & Teixeira, A. (2012) · Theory of Computing Systems · doi:10.1007/s00224-012-9418-z · Cited by 6
A comparison of two approaches to pseudorandomness
Wang, Y. (2002) · Theoretical Computer Science · doi:10.1016/s0304-3975(01)00311-5 · Cited by 6
Can Machines Think? An Old Question Reformulated
Hoffmann, A. G. (2010) · Minds and Machines · doi:10.1007/s11023-010-9193-z · Cited by 6
Generation of Folk Song Melodies using Bayes Transforms
Thornton, C. (2011) · Journal of New Music Research · doi:10.1080/09298215.2011.573559 · Cited by 6
Philosophers & Futurists, Catch Up! Response to The Singularity
Schmidhuber, J. (2012) · Cited by 6
HIGHER RANDOMNESS AND GENERICITY
Greenberg, N., & MONIN, B. (2017) · Forum of Mathematics Sigma · doi:10.1017/fms.2017.27 · Cited by 6
Computational Creativity and Aesthetics with Algorithmic Information Theory
Mondol, T., & Brown, D. G. (2021) · Entropy · doi:10.3390/e23121654 · Cited by 6
Design of Superior Parameterized Quantum Circuits for Quantum Image Classification
Mishra, S., & Tsai, C. (2022) · International Conference on Computer and Automation Engineering · doi:10.1109/iccae55086.2022.9762420 · Cited by 7
Is Darwinian selection a retrograde driving force of evolution?
Vitas, M., & Dobovišek, A. (2023) · Biosystems · doi:10.1016/j.biosystems.2023.105031 · Cited by 6
Memory Issues of Intelligent Agents
Orseau, L., & Ring, M. B. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-35506-6_23 · Cited by 6
Complexity formalisms, order and disorder in the structure of art
Davis, M. W. (1997) · Lecture notes in computer science · doi:10.1007/bfb0014796 · Cited by 6
Bicompletions of Distance Matrices
Pavlović, D. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-38164-5_20 · Cited by 6
Complexity Measures for Meta-learning and Their Optimality
Jankowski, N. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-44958-1_15 · Cited by 6
Comparing Security Notions of Secret Sharing Schemes
Dai, S., & Guo, D. (2015) · Entropy · doi:10.3390/e17031135 · Cited by 6
Computer vision: Compress to comprehend
Nohre, R. (1995) · Pattern Recognition Letters · doi:10.1016/0167-8655(95)00027-e · Cited by 6
Algorithmic Randomness and Measures of Complexity
Barmpalias, G. (2013) · Bulletin of Symbolic Logic · doi:10.1017/s1079898600010672 · Cited by 6
Thoughts on Computational Creativity
Galanter, P. (2009) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/dagsemproc.09291.32 · Cited by 6
Goertzel, B. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-41649-6_35 · Cited by 6
Algorithmic complexity of quantum capacity
Oskouei, S. K., & Mancini, S. (2018) · Quantum Information Processing · doi:10.1007/s11128-018-1859-0 · Cited by 6
Continually adding self-invented problems to the repertoire: First experiments with POWERPLAY
Srivastava, R. K., Steunebrink, B. R., Stollenga, M., & Schmidhuber, J. (2012) · IEEE International Conference on Development and Learning (ICDL) · doi:10.1109/devlrn.2012.6400843 · Cited by 6
An Overview of Approaches Evaluating Intelligence of Artificial Systems
Vadinský, O. (2018) · Acta Informatica Pragensia · doi:10.18267/j.aip.115 · Cited by 6
Origins of the Embryo: Self-organization through cybernetic regulation
Stone, R., Portegys, T., Mikhailovsky, G. E., & Alicea, B. (2018) · Biosystems · doi:10.1016/j.biosystems.2018.08.005 · Cited by 6
Key developments in algorithmic randomness
Franklin, J. N. Y., & Porter, C. P. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108781718.002 · Cited by 6
Towards a more flexible language of thought: Bayesian grammar updates after each concept exposure
Tano, P., Romano, S., Sigman, M., Salles, A., & Figueira, S. D. (2020) · Physical review. E · doi:10.1103/physreve.101.042128 · Cited by 6
Moreno‐Casas, V. (2021) · The Review of Austrian Economics · doi:10.1007/s11138-021-00565-6 · Cited by 7
The role of computational intelligence in data mining
Rubin, S. H., Ceruti, M. G., & Dai, W. (2005) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri-05.2005.1506472 · Cited by 6
Sophistication vs Logical Depth
Antunes, L. F., Bauwens, B., Souto, A., & Teixeira, A. (2016) · Theory of Computing Systems · doi:10.1007/s00224-016-9672-6 · Cited by 6
Enhancing MML Clustering Using Context Data with Climate Applications
Visser, G., Dowe, D. L., & Uotila, P. J. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-10439-8_36 · Cited by 6
Kolmogorov Complexity and the Information Content of Parameters
Clark, R. (1994) · Scholarly Commons (University of Pennsylvania) · Cited by 6
Parsimony Doesn’t Mean Simplicity: Genetic Programming for Inductive Inference on Noisy Data
Falco, I. D., Cioppa, A. D., Maisto, D., Scafuri, U., & Tarantino, E. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-71605-1_33 · Cited by 6
Sequence prediction for non-stationary processes
Ryabko, D., & Hütter, M. (2006) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/dagsemproc.06201.6 · Cited by 6
Connectionist functionality and the emergent network behavior
Hoffmann, A. G. (1991) · Neurocomputing · doi:10.1016/0925-2312(91)90046-e · Cited by 6
Pavlović, D. (2015) · Symposium and Bootcamp on Science of Security · doi:10.1145/2746194.2746197 · Cited by 6
Statistical learning theory, model identification and system information content
Guergachi, A., & Patry, G. G. (2002) · International Journal of General Systems · doi:10.1080/03081070290018056 · Cited by 6
PHYSICAL COMPLEXITY AND COGNITIVE EVOLUTION
Jedlička, P. (2007) · doi:10.1142/9789812707420_0014 · Cited by 6
Optimality of universal Bayesian sequence prediction for general loss and alphabet
HutterMarcus (2003) · ANU Open Research (Australian National University) · doi:10.1162/1532443041827952 · Cited by 6
Normalized Compression Distance Based Measures for MetricsMATR 2010
Dobrinkat, M., Tapiovaara, T., Väyrynen, J. J., & Kettunen, K. (2010) · Cited by 6
Too much information can be too much for learning efficiently
Wiehagen, R., & Zeugmann, T. (1992) · Lecture notes in computer science · doi:10.1007/3-540-56004-1_5 · Cited by 6
Neural Networks: Automata and Formal Models of Computation
Forcada, M. L. (2002) · Cited by 6
Ockham's Razor at Work: Modeling of the ``Homunculus''
Lörincz, A., Póczos, B., Szirtes, G., & Takács, B. M. (2002) · Brain and Mind · doi:10.1023/a:1019996320835 · Cited by 6
Fulop, S. A., & Chater, N. (2013) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.1228 · Cited by 6
Consciousness is Data Compression
Maguire, P., & Maguire, R. (2010) · eScholarship (California Digital Library) · Cited by 6
Measuring the Difficulty of Specific Learning Problems
Thornton, C. (1995) · Connection Science · doi:10.1080/09540099508915658 · Cited by 6
Descriptive complexity of computable sequences
Durand, B., Shen', A. K., & Vereshchagin, N. (2002) · Theoretical Computer Science · doi:10.1016/s0304-3975(01)00030-5 · Cited by 6
Conceptual Clumping of Binary Vectors with Occam's Razor
Segen, J. (1988) · Elsevier eBooks · doi:10.1016/b978-0-934613-64-4.50010-4 · Cited by 6
Degrees of monotone complexity
Calhoun, W. C. (2006) · Journal of Symbolic Logic · doi:10.2178/jsl/1164060458 · Cited by 6
Random languages for nonuniform complexity classes
Mundhenk, M., & Schuler, R. (1991) · Journal of Complexity · doi:10.1016/0885-064x(91)90038-y · Cited by 6
On the problem of stable image restoration
Terebizh, V. Y., & Biryukov, V. V. (1994) · Astrophysics and Space Science · doi:10.1007/bf00658067 · Cited by 6
Neural Mechanisms for Information Compression by Multiple Alignment, Unification and SearchPreprint
Wolff, J. G. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.3076042 · Cited by 6
Cognitive and Communicative Development in Reality Free Representation
Diettrich, O. (1994) · Intellectica Revue de l Association pour la Recherche Cognitive · doi:10.3406/intel.1994.1452 · Cited by 6
Quantum Kolmogorov Complexity and the Quantum Turing Machine
Müller, M. P. (2007) · DepositOnce · doi:10.14279/depositonce-1687 · Cited by 6
A Simple Overview of Complex Systems and Complexity Measures
Monteiro, L. H. A. (2025) · Complexities · doi:10.3390/complexities1010002 · Cited by 5
Frequentist Interpretation of Probability
Bickel, P. J., & Lehmann, E. L. (2011) · Springer eBooks · doi:10.1007/978-1-4614-1412-4_92 · Cited by 5
Process and truth-table characterisations of randomness
Day, A. R. (2012) · Theoretical Computer Science · doi:10.1016/j.tcs.2012.05.028 · Cited by 5
On Empirical Meaning of Randomness with Respect to Parametric Families of Probability Distributions
V'yugin, V. V. (2010) · Theory of Computing Systems · doi:10.1007/s00224-010-9300-9 · Cited by 5
Shimansky, Y. P. (2004) · Minds and Machines · doi:10.1023/b:mind.0000045988.12140.9f · Cited by 5
The value of compression for taxonomic identification
Silva, J. M., & Almeida, J. R. (2022) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms55023.2022.00055 · Cited by 5
Universal Prediction of Selected Bits
Lattimore, T., Hütter, M., & Gavane, V. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24412-4_22 · Cited by 5
Algorithmic analysis of irrational rotations in a single neuron model
Takahashi, H., & Aihara, K. (2003) · Journal of Complexity · doi:10.1016/s0885-064x(02)00017-1 · Cited by 5
Comparing Notions of Computational Entropy
Pinto, A. (2009) · Theory of Computing Systems · doi:10.1007/s00224-009-9177-7 · Cited by 5
AN ALGORITHMIC INFORMATION THEORY CHALLENGE TO INTELLIGENT DESIGN
Devine, S. (2014) · Zygon® · doi:10.1111/zygo.12059 · Cited by 5
On the Inherent Incompleteness of Scientific Theories
Mathen, J. (2011) · Activitas Nervosa Superior · doi:10.1007/bf03379933 · Cited by 5
A Method to Detect Repeated Unknown Patterns in an Image
Ferreira, P. J. S. G., & Pinho, A. J. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-11758-4_2 · Cited by 5
Clinical Bioinformatics for Biomarker Discovery in Targeted Metabolomics
Breit, M., Baumgärtner, C., Netzer, M., & Weinberger, K. M. (2016) · Translational bioinformatics · doi:10.1007/978-94-017-7543-4_8 · Cited by 5
Universal prediction of individual sequences
Feder, M., Merhav, N., & Gutman, M. (2002) · IEEE Convention of Electrical and Electronics Engineers in Israel · doi:10.1109/eeis.1991.217658 · Cited by 5
Very Simple Chaitin Machines for Concrete AIT
Stay, M. (2005) · Fundamenta Informaticae · doi:10.3233/fun-2005-68303 · Cited by 5
QKSA: Quantum Knowledge Seeking Agent
Sarkar, A., Al-Ars, Z., & Bertels, K. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-19907-3_37 · Cited by 5
Lutz, N., & Stull, D. M. (2021) · Computability · doi:10.3233/com-190292 · Cited by 5
Kolmogorov’s Complexity Conception of Probability
Vovk, V. (2001) · Springer eBooks · doi:10.1007/978-94-015-9648-0_4 · Cited by 5
Metadata
- Authors
- Solomonoff, R. J.
- Corresponding author
- Solomonoff, R.J.
- Venue
- Information and Control
- Publisher
- Elsevier BV
- Published
- 1964-06
- Volume
- 7
- Issue
- 2
- Pages
- 224-254
- ISSN
- 0019-9958 (ISSN-L)
- OpenAlex
- W2135625884
- Cited by
- 1,758
- References
- 6
- Citations by year
- 2026: 43 · 2025: 16 · 2024: 18 · 2023: 22 · 2022: 35 · 2021: 52 · 2020: 60 · 2019: 40 · 2018: 60 · 2017: 67 · 2016: 64 · 2015: 75 · 2014: 59 · 2013: 94 · 2012: 88
- FWCI
- 1.52
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Information and Control (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computability, Logic, AI Algorithms
- Physical Sciences › Computer Science › Computational Theory and Mathematics › semigroups and automata theory
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Keywords
- inductive reasoning, universal Turing machine, phrase structure grammar, prediction accuracy
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
