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Entropy
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Entropy in Experimental Design, Sensor Placement, Inquiry and Search
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Entropy in Experimental Design, Sensor Placement, Inquiry and Search

A special issue of Entropy (ISSN 1099-4300).

Deadline for manuscript submissions: closed (1 March 2015) | Viewed by 32751

Special Issue Editor

Prof. Dr. Kevin H. Knuth

E-Mail Website
Guest Editor
Department of Physics, University at Albany, 1400 Washington Avenue, Albany, NY 12222, USA
Interests: bayesian data analysis; entropy; probability theory; signal processing; machine learning; robotics; foundations of physics; quantum information; exoplanet detection and characterization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Collegues,

Entropy, as a measure of uncertainty or missing information, naturally quantifies the process of inquiry. Whether the act of inquiry involves formal questions, verbal requests for information, intelligent search, sensor placement or experimental design, entropy promises to play a prominent role in optimizing these activities by allowing one to quantify the relevance of an act of inquiry. This special issue will bring together researchers who have  performed theoretical research in exploring the role of entropy in optimizing relevance, as well as practitioners who have used maximum entropy methods in experimental design, sensor placement and intelligent search.

Prof. Dr. Kevin H. Knuth
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


Keywords

  • entropy
  • erotetic logic
  • experimental design
  • information
  • inquiry
  • relative entropy
  • relevance
  • search
  • sensor placement
  • Shannon information
  • questions

Published Papers (5 papers)

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Research

1233 KiB  
Article
Fully Bayesian Experimental Design for Pharmacokinetic Studies
by Elizabeth G. Ryan, Christopher C. Drovandi and Anthony N. Pettitt
Entropy 2015, 17(3), 1063-1089; https://0-doi-org.brum.beds.ac.uk/10.3390/e17031063 - 05 Mar 2015
Cited by 30 | Viewed by 6605
Abstract
Utility functions in Bayesian experimental design are usually based on the posterior distribution. When the posterior is found by simulation, it must be sampled from for each future dataset drawn from the prior predictive distribution. Many thousands of posterior distributions are often required. [...] Read more.
Utility functions in Bayesian experimental design are usually based on the posterior distribution. When the posterior is found by simulation, it must be sampled from for each future dataset drawn from the prior predictive distribution. Many thousands of posterior distributions are often required. A popular technique in the Bayesian experimental design literature, which rapidly obtains samples from the posterior, is importance sampling, using the prior as the importance distribution. However, importance sampling from the prior will tend to break down if there is a reasonable number of experimental observations. In this paper, we explore the use of Laplace approximations in the design setting to overcome this drawback. Furthermore, we consider using the Laplace approximation to form the importance distribution to obtain a more efficient importance distribution than the prior. The methodology is motivated by a pharmacokinetic study, which investigates the effect of extracorporeal membrane oxygenation on the pharmacokinetics of antibiotics in sheep. The design problem is to find 10 near optimal plasma sampling times that produce precise estimates of pharmacokinetic model parameters/measures of interest. We consider several different utility functions of interest in these studies, which involve the posterior distribution of parameter functions. Full article
(This article belongs to the Special Issue Entropy in Experimental Design, Sensor Placement, Inquiry and Search)
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3044 KiB  
Article
Hierarchical Sensor Placement Using Joint Entropy and the Effect of Modeling Error
by Maria Papadopoulou, Benny Raphael, Ian F.C. Smith and Chandra Sekhar
Entropy 2014, 16(9), 5078-5101; https://0-doi-org.brum.beds.ac.uk/10.3390/e16095078 - 23 Sep 2014
Cited by 41 | Viewed by 7650
Abstract
Good prediction of the behavior of wind around buildings improves designs for natural ventilation in warm climates. However wind modeling is complex, predictions are often inaccurate due to the large uncertainties in parameter values. The goal of this work is to enhance wind [...] Read more.
Good prediction of the behavior of wind around buildings improves designs for natural ventilation in warm climates. However wind modeling is complex, predictions are often inaccurate due to the large uncertainties in parameter values. The goal of this work is to enhance wind prediction around buildings using measurements through implementing a multiple-model system-identification approach. The success of system-identification approaches depends directly upon the location and number of sensors. Therefore, this research proposes a methodology for optimal sensor configuration based on hierarchical sensor placement involving calculations of prediction-value joint entropy. Computational Fluid Dynamics (CFD) models are generated to create a discrete population of possible wind-flow predictions, which are then used to identify optimal sensor locations. Optimal sensor configurations are revealed using the proposed methodology and considering the effect of systematic and spatially distributed modeling errors, as well as the common information between sensor locations. The methodology is applied to a full-scale case study and optimum configurations are evaluated for their ability to falsify models and improve predictions at locations where no measurements have been taken. It is concluded that a sensor placement strategy using joint entropy is able to lead to predictions of wind characteristics around buildings and capture short-term wind variability more effectively than sequential strategies, which maximize entropy. Full article
(This article belongs to the Special Issue Entropy in Experimental Design, Sensor Placement, Inquiry and Search)
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738 KiB  
Article
Effect of Conformational Entropy on the Nanomechanics of Microcantilever-Based Single-Stranded DNA Sensors
by Zou-Qing Tan and Neng-Hui Zhang
Entropy 2014, 16(9), 4923-4936; https://0-doi-org.brum.beds.ac.uk/10.3390/e16094923 - 15 Sep 2014
Cited by 1 | Viewed by 4885
Abstract
An entropy-controlled bending mechanism is presented to study the nanomechanics of microcantilever-based single-stranded DNA (ssDNA) sensors. First; the conformational free energy of the ssDNA layer is given with an improved scaling theory of thermal blobs considering the curvature effect; and the mechanical energy [...] Read more.
An entropy-controlled bending mechanism is presented to study the nanomechanics of microcantilever-based single-stranded DNA (ssDNA) sensors. First; the conformational free energy of the ssDNA layer is given with an improved scaling theory of thermal blobs considering the curvature effect; and the mechanical energy of the non-biological layer is described by Zhang’s two-variable method for laminated beams. Then; an analytical model for static deflections of ssDNA microcantilevers is formulated by the principle of minimum energy. The comparisons of deflections predicted by the proposed model; Utz–Begley’s model and Hagan’s model are also examined. Numerical results show that the conformational entropy effect on microcantilever deflections cannot be ignored; especially at the conditions of high packing density or long chain systems; and the variation of deflection predicted by the proposed analytical model not only accords with that observed in the related experiments qualitatively; but also appears quantitatively closer to the experimental values than that by the preexisting models. In order to improve the sensitivity of static-mode biosensors; it should be as small as possible to reduce the substrate stiffness. Full article
(This article belongs to the Special Issue Entropy in Experimental Design, Sensor Placement, Inquiry and Search)
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215 KiB  
Article
Learning Functions and Approximate Bayesian Computation Design: ABCD
by Markus Hainy, Werner G. Müller and Henry P. Wynn
Entropy 2014, 16(8), 4353-4374; https://0-doi-org.brum.beds.ac.uk/10.3390/e16084353 - 04 Aug 2014
Cited by 8 | Viewed by 5176
Abstract
A general approach to Bayesian learning revisits some classical results, which study which functionals on a prior distribution are expected to increase, in a preposterior sense. The results are applied to information functionals of the Shannon type and to a class of functionals [...] Read more.
A general approach to Bayesian learning revisits some classical results, which study which functionals on a prior distribution are expected to increase, in a preposterior sense. The results are applied to information functionals of the Shannon type and to a class of functionals based on expected distance. A close connection is made between the latter and a metric embedding theory due to Schoenberg and others. For the Shannon type, there is a connection to majorization theory for distributions. A computational method is described to solve generalized optimal experimental design problems arising from the learning framework based on a version of the well-known approximate Bayesian computation (ABC) method for carrying out the Bayesian analysis based on Monte Carlo simulation. Some simple examples are given. Full article
(This article belongs to the Special Issue Entropy in Experimental Design, Sensor Placement, Inquiry and Search)
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788 KiB  
Article
Tsallis Wavelet Entropy and Its Application in Power Signal Analysis
by Jikai Chen and Guoqing Li
Entropy 2014, 16(6), 3009-3025; https://0-doi-org.brum.beds.ac.uk/10.3390/e16063009 - 27 May 2014
Cited by 56 | Viewed by 7667
Abstract
As a novel data mining approach, a wavelet entropy algorithm is used to perform entropy statistics on wavelet coefficients (or reconstructed signals) at various wavelet scales on the basis of wavelet decomposition and entropy statistic theory. Shannon wavelet energy entropy, one kind of [...] Read more.
As a novel data mining approach, a wavelet entropy algorithm is used to perform entropy statistics on wavelet coefficients (or reconstructed signals) at various wavelet scales on the basis of wavelet decomposition and entropy statistic theory. Shannon wavelet energy entropy, one kind of wavelet entropy algorithm, has been taken into consideration and utilized in many areas since it came into being. However, as there is wavelet aliasing after the wavelet decomposition, and the information set of different-scale wavelet decomposition coefficients (or reconstructed signals) is non-additive to a certain extent, Shannon entropy, which is more adaptable to extensive systems, couldn’t do accurate uncertainty statistics on the wavelet decomposition results. Therefore, the transient signal features are extracted incorrectly by using Shannon wavelet energy entropy. From the two aspects, the theoretical limitations and negative effects of wavelet aliasing on extraction accuracy, the problems which exist in the feature extraction process of transient signals by Shannon wavelet energy entropy, are discussed in depth. Considering the defects of Shannon wavelet energy entropy, a novel wavelet entropy named Tsallis wavelet energy entropy is proposed by using Tsallis entropy instead of Shannon entropy, and it is applied to the feature extraction of transient signals in power systems. Theoretical derivation and experimental result prove that compared with Shannon wavelet energy entropy, Tsallis wavelet energy entropy could reduce the negative effects of wavelet aliasing on accuracy of feature extraction and extract transient signal feature of power system accurately. Full article
(This article belongs to the Special Issue Entropy in Experimental Design, Sensor Placement, Inquiry and Search)
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