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2
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Three Kinds Of Probabilistic Induction: Universal Distributions and Convergence Theorems
– Ray J. Solomonoff
- 2003
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22
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The discovery of algorithmic probability
– Ray J. Solomonoff, Royal Holloway
- 1997
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3
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Does Algorithmic Probability Solve the Problem of Induction?
– Ray Solomonoff
- 2001
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Algorithmic Probability—Theory and Applications
– Ray J. Solomonoff
- 2009
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4
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Computational Machine Learning in Theory and Praxis
– Ming Li, Paul Vitányi
- 1995
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41
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A tutorial introduction to the minimum description length principle
– Peter Grünwald
|
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Randomness
– Paul Vitányi
- 1995
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Simplicity, Information, Kolmogorov Complexity, and Prediction
– Paul Vitányi, Ming Li
- 1998
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4
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Open Problems in Universal Induction & Intelligence
– Marcus Hutter
- 2009
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17
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Applying MDL to Learning Best Model Granularity
– Qiong Gao, Ming Li, Paul Vitányi
- 1994
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60
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Minimum Description Length Induction, Bayesianism, and Kolmogorov Complexity
– Paul Vitányi, Ming Li
- 1998
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9
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On Prediction by Data Compression
– Paul Vitányi, Ming Li
- 1997
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15
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Algorithmic Complexity and Stochastic Properties of Finite Binary Sequences
– V.V. V'Yugin
- 1999
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18
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Lecture Notes on Descriptional Complexity and Randomness
– Peter Gács
|
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14
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The Generalized Universal Law of Generalization
– Nick Chater, Paul M.B. Vitányi
- 2001
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unknown title
– Raymond J. Solomonoff, Peter Gács, Paul M. B. Vitányi
- 2010
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Introducing the Minimum Description Length Principle
– Peter Grünwald
|
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1
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Schwarz, Wallace, and Rissanen: Intertwining Themes in Theories of Model Selection
– Aaron D. Lanterman
- 2000
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Short Course: Introduction to Information-Theoretic Modeling Fifth Brazilian Conference on Statistical Modelling in Insurance and Finance
– Teemu Roos
- 2011
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