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Extraction of Quantifiable Information from Complex Systems

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Cover of 'Extraction of Quantifiable Information from Complex Systems'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Solving Stochastic Dynamic Programs by Convex Optimization and Simulation
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    Chapter 2 Efficient Resolution of Anisotropic Structures
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    Chapter 3 Regularity of the Parameter-to-State Map of a Parabolic Partial Differential Equation
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    Chapter 4 Piecewise Tensor Product Wavelet Bases by Extensions and Approximation Rates
  6. Altmetric Badge
    Chapter 5 Adaptive Wavelet Methods for SPDEs
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    Chapter 6 Constructive Quantization and Multilevel Algorithms for Quadrature of Stochastic Differential Equations
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    Chapter 7 Bayesian Inverse Problems and Kalman Filters
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    Chapter 8 Robustness in Stochastic Filtering and Maximum Likelihood Estimation for SDEs
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    Chapter 9 Adaptive Sparse Grids in Reinforcement Learning
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    Chapter 10 A Review on Adaptive Low-Rank Approximation Techniques in the Hierarchical Tensor Format
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    Chapter 11 A Bond Order Dissection ANOVA Approach for Efficient Electronic Structure Calculations
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    Chapter 12 Tensor Spaces and Hierarchical Tensor Representations
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    Chapter 13 Nonlinear Eigenproblems in Data Analysis: Balanced Graph Cuts and the RatioDCA-Prox
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    Chapter 14 Adaptive Approximation Algorithms for Sparse Data Representation
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    Chapter 15 Error Bound for Hybrid Models of Two-Scaled Stochastic Reaction Systems
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    Chapter 16 Valuation of Structured Financial Products by Adaptive Multiwavelet Methods in High Dimensions
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    Chapter 17 Computational Methods for the Fourier Analysis of Sparse High-Dimensional Functions
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    Chapter 18 Sparsity and Compressed Sensing in Inverse Problems
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    Chapter 19 Low-Rank Dynamics
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    Chapter 20 Computation of Expectations by Markov Chain Monte Carlo Methods
  22. Altmetric Badge
    Chapter 21 Regularity, Complexity, and Approximability of Electronic Wavefunctions
Attention for Chapter 20: Computation of Expectations by Markov Chain Monte Carlo Methods
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About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • High Attention Score compared to outputs of the same age and source (89th percentile)

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Citations

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Chapter title
Computation of Expectations by Markov Chain Monte Carlo Methods
Chapter number 20
Book title
Extraction of Quantifiable Information from Complex Systems
Published in
arXiv, January 2014
DOI 10.1007/978-3-319-08159-5_20
Book ISBNs
978-3-31-908158-8, 978-3-31-908159-5
Authors

Erich Novak, Daniel Rudolf, Novak, Erich, Rudolf, Daniel

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 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 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 20%
Unknown 4 80%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 40%
Professor > Associate Professor 2 40%
Professor 1 20%
Readers by discipline Count As %
Mathematics 3 60%
Computer Science 2 40%
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 12 November 2013.
All research outputs
#7,435,148
of 22,729,647 outputs
Outputs from arXiv
#167,570
of 932,128 outputs
Outputs of similar age
#91,848
of 305,164 outputs
Outputs of similar age from arXiv
#944
of 9,916 outputs
Altmetric has tracked 22,729,647 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 932,128 research outputs from this source. They receive a mean Attention Score of 3.9. 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 305,164 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 9,916 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.