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Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence

Overview of attention for book
Cover of 'Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    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
Overall attention for this book and its chapters
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Mentioned by

news
3 news outlets
blogs
1 blog
twitter
1 tweeter

Citations

dimensions_citation
8 Dimensions

Readers on

mendeley
16 Mendeley
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Title
Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence
Published by
Lecture notes in computer science, January 2013
DOI 10.1007/978-3-642-44958-1
ISBNs
978-3-64-244957-4, 978-3-64-244958-1
Editors

David L. Dowe

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 25%
Researcher 3 19%
Student > Ph. D. Student 2 13%
Student > Doctoral Student 1 6%
Student > Bachelor 1 6%
Other 3 19%
Unknown 2 13%
Readers by discipline Count As %
Computer Science 6 38%
Engineering 3 19%
Social Sciences 2 13%
Biochemistry, Genetics and Molecular Biology 1 6%
Physics and Astronomy 1 6%
Other 0 0%
Unknown 3 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 34. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 31 July 2021.
All research outputs
#844,745
of 20,206,018 outputs
Outputs from Lecture notes in computer science
#117
of 7,990 outputs
Outputs of similar age
#16,966
of 277,225 outputs
Outputs of similar age from Lecture notes in computer science
#3
of 195 outputs
Altmetric has tracked 20,206,018 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,990 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done particularly well, scoring higher than 98% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 277,225 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 195 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 98% of its contemporaries.