Two Kinds of Probabilistic Induction (1999)

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by Ray J. Solomonoff
Venue:COMPUTER JOURNAL
Citations:9 - 1 self

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2 Three Kinds Of Probabilistic Induction: Universal Distributions and Convergence Theorems – Ray J. Solomonoff - 2003
41 The discovery of algorithmic probability – Ray J. Solomonoff - 1997
5 Does Algorithmic Probability Solve the Problem of Induction? – Ray Solomonoff - 2001
Algorithmic Probability—Theory and Applications – Ray J. Solomonoff - 2009
4 Computational Machine Learning in Theory and Praxis – Ming Li, Paul Vitányi - 1995
58 A tutorial introduction to the minimum description length principle – Peter Grünwald
46 A simplicity principle in unsupervised human categorization – Emmanuel M. Pothos , Nick Chater - 2002
Randomness – Paul Vitányi - 1995
Simplicity, Information, Kolmogorov Complexity, and Prediction – Paul Vitányi, Ming Li - 1998
10 On Prediction by Data Compression – Paul Vitányi, Ming Li - 1997
5 Open problems in universal induction & intelligence – Marcus Hutter - 2009
20 Applying MDL to Learning Best Model Granularity – Qiong Gao, Ming Li, Paul Vitányi - 1994
67 Minimum Description Length Induction, Bayesianism, and Kolmogorov Complexity – Paul Vitányi, Ming Li - 1998
21 Lecture Notes on Descriptional Complexity and Randomness – Peter Gács
17 Algorithmic Complexity and Stochastic Properties of Finite Binary Sequences – V.V. V'Yugin - 1999
19 The Generalized Universal Law of Generalization – Nick Chater, Paul M.B. Vitányi - 2001
4 EXPLANATORY AND CREATIVE ALTERNATIVES TO THE MDL PRINCIPLE – JOSÉ HERNÁNDEZ-ORALLO , ISMAEL GARCÍA-VAREA - 2000
unknown title – Raymond J. Solomonoff, Peter Gács, Paul M. B. Vitányi - 2011
unknown title – Raymond J. Solomonoff, Peter Gács, Paul M. B. Vitányi - 2010