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Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence

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Cover of 'Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence'

Table of Contents

  1. Altmetric Badge
    Book Overview
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    Chapter 1 Introduction to Ray Solomonoff 85th Memorial Conference
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    Chapter 2 Ray Solomonoff and the New Probability
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    Chapter 3 Universal Heuristics: How Do Humans Solve “Unsolvable” Problems?
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    Chapter 4 Partial Match Distance
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    Chapter 5 Falsification and Future Performance
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    Chapter 6 The Semimeasure Property of Algorithmic Probability – “Feature” or “Bug”?
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    Chapter 7 Inductive Inference and Partition Exchangeability in Classification
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    Chapter 8 Learning in the Limit: A Mutational and Adaptive Approach
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    Chapter 9 Algorithmic Simplicity and Relevance
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    Chapter 10 Categorisation as Topographic Mapping between Uncorrelated Spaces
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    Chapter 11 Algorithmic Information Theory and Computational Complexity
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    Chapter 12 A Critical Survey of Some Competing Accounts of Concrete Digital Computation
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    Chapter 13 Further Reflections on the Timescale of AI
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    Chapter 14 Towards Discovering the Intrinsic Cardinality and Dimensionality of Time Series Using MDL
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    Chapter 15 Complexity Measures for Meta-learning and Their Optimality
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    Chapter 16 Design of a Conscious Machine
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    Chapter 17 No Free Lunch versus Occam’s Razor in Supervised Learning
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    Chapter 18 An Approximation of the Universal Intelligence Measure
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    Chapter 19 Minimum Message Length Analysis of the Behrens–Fisher Problem
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    Chapter 20 MMLD Inference of Multilayer Perceptrons
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    Chapter 21 An Optimal Superfarthingale and Its Convergence over a Computable Topological Space
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    Chapter 22 Diverse Consequences of Algorithmic Probability
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    Chapter 23 An Adaptive Compression Algorithm in a Deterministic World
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    Chapter 24 Toward an Algorithmic Metaphysics
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    Chapter 25 Limiting Context by Using the Web to Minimize Conceptual Jump Size
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    Chapter 26 Minimum Message Length Order Selection and Parameter Estimation of Moving Average Models
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    Chapter 27 Abstraction Super-Structuring Normal Forms: Towards a Theory of Structural Induction
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    Chapter 28 Locating a Discontinuity in a Piecewise-Smooth Periodic Function Using Bayes Estimation
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    Chapter 29 On the Application of Algorithmic Probability to Autoregressive Models
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    Chapter 30 Principles of Solomonoff Induction and AIXI
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    Chapter 31 MDL/Bayesian Criteria Based on Universal Coding/Measure
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    Chapter 32 Algorithmic Analogies to Kamae-Weiss Theorem on Normal Numbers
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    Chapter 33 (Non-)Equivalence of Universal Priors
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    Chapter 34 A Syntactic Approach to Prediction
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    Chapter 35 Developing Machine Intelligence within P2P Networks Using a Distributed Associative Memory
Attention for Chapter 24: Toward an Algorithmic Metaphysics
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Chapter title
Toward an Algorithmic Metaphysics
Chapter number 24
Book title
Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence
Published in
Lecture notes in computer science, January 2013
DOI 10.1007/978-3-642-44958-1_24
Book ISBNs
978-3-64-244957-4, 978-3-64-244958-1
Authors

Steve Petersen

Editors

David L. Dowe

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 100%
Professor > Associate Professor 1 50%
Researcher 1 50%
Student > Master 1 50%
Readers by discipline Count As %
Philosophy 4 200%