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Bayesian Inference and Maximum Entropy Methods in Science and Engineering

Overview of attention for book
Cover of 'Bayesian Inference and Maximum Entropy Methods in Science and Engineering'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Quantum Phases in Entropic Dynamics
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    Chapter 2 Bayesian Approach to Variable Splitting Forward Models
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    Chapter 3 Prior Shift Using the Ratio Estimator
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    Chapter 4 Bayesian Meta-Analytic Measure
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    Chapter 5 Feature Selection from Local Lift Dependence-Based Partitions
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    Chapter 6 Probabilistic Inference of Surface Heat Flux Densities from Infrared Thermography
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    Chapter 7 Schrödinger’s Zebra: Applying Mutual Information Maximization to Graphical Halftoning
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    Chapter 8 Regression of Fluctuating System Properties: Baryonic Tully–Fisher Scaling in Disk Galaxies
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    Chapter 9 Bayesian Portfolio Optimization for Electricity Generation Planning
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    Chapter 10 Bayesian Variable Selection Methods for Log-Gaussian Cox Processes
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    Chapter 11 Effect of Hindered Diffusion on the Parameter Sensitivity of Magnetic Resonance Spectra
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    Chapter 12 The Random Bernstein Polynomial Smoothing Via ABC Method
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    Chapter 13 Mean Field Studies of a Society of Interacting Agents
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    Chapter 14 The Beginnings of Axiomatic Subjective Probability
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    Chapter 15 Model Selection in the Sparsity Context for Inverse Problems in Bayesian Framework
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    Chapter 16 Sample Size Calculation Using Decision Theory
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    Chapter 17 Utility for Significance Tests
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    Chapter 18 Probabilistic Equilibrium: A Review on the Application of MAXENT to Macroeconomic Models
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    Chapter 19 Full Bayesian Approach for Signal Detection with An Application to Boat Detection on Underwater Soundscape Data
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    Chapter 20 Bayesian Support for Evolution: Detecting Phylogenetic Signal in a Subset of the Primate Family
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    Chapter 21 A Comparison of Two Methods for Obtaining a Collective Posterior Distribution
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    Chapter 22 A Nonparametric Bayesian Approach for the Two-Sample Problem
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    Chapter 23 Covariance Modeling for Multivariate Spatial Processes Based on Separable Approximations
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    Chapter 24 Uncertainty Quantification and Cumulative Distribution Function: How are they Related?
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    Chapter 25 Maximum Entropy Analysis of Flow Networks with Structural Uncertainty (Graph Ensembles)
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    Chapter 26 Optimization Employing Gaussian Process-Based Surrogates
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    Chapter 27 Bayesian and Maximum Entropy Analyses of Flow Networks with Non-Gaussian Priors and Soft Constraints
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    Chapter 28 Using the Z-Order Curve for Bayesian Model Comparison
Attention for Chapter 20: Bayesian Support for Evolution: Detecting Phylogenetic Signal in a Subset of the Primate Family
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (52nd percentile)
  • Good Attention Score compared to outputs of the same age and source (77th percentile)

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Chapter title
Bayesian Support for Evolution: Detecting Phylogenetic Signal in a Subset of the Primate Family
Chapter number 20
Book title
Bayesian Inference and Maximum Entropy Methods in Science and Engineering
Published in
arXiv, July 2017
DOI 10.1007/978-3-319-91143-4_20
Book ISBNs
978-3-31-991142-7, 978-3-31-991143-4
Authors

Patricio Maturana Russel, Maturana Russel, Patricio

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
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%
Readers by discipline Count As %
Agricultural and Biological Sciences 1 50%
Social Sciences 1 50%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 16 September 2017.
All research outputs
#13,668,543
of 24,220,739 outputs
Outputs from arXiv
#196,412
of 1,027,648 outputs
Outputs of similar age
#148,860
of 316,053 outputs
Outputs of similar age from arXiv
#3,932
of 19,095 outputs
Altmetric has tracked 24,220,739 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,027,648 research outputs from this source. They receive a mean Attention Score of 4.0. This one has done well, scoring higher than 80% 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 316,053 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.
We're also able to compare this research output to 19,095 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.