Special Issue "Estimating Information-Theoretic Quantities from Data"
A special issue of Entropy (ISSN 1099-4300).
Deadline for manuscript submissions: closed (31 January 2013)
Dr. Ilya Nemenman
Theoretical Biophysics Laboratory, Department of Physics and Department of Biology, Emory University, Atlanta GA 30322, USA
Interests: information processing in biological systems; coarse-grained modeling in biology
Information-theoretic methods have become a workhorse of interdisciplinary research in computational molecular biology, computational neuroscience, ecology, social communications, and other fields. They are used for inference of interaction networks (such as protein networks or neural wiring diagrams), for understanding communication within these networks, and for building dynamical models of input-output behavior in them. They are further used to quantify diversity and stability of ecological niches, to characterize social interactions among individuals, and to develop assays for diseases and other abnormalities. One of the key problems slowing wider acceptance of these methods is the difficulty of reliable estimation of entropy and other information-theoretic quantities from empirical data. The field has made a remarkable progress in this direction in the recent years, and this Special Issue will explore this progress. We welcome contributions that explore methodological and algorithmic advances, applications to specialized data-driven research problems in the various fields of science, and theoretical investigations that explore the limits of our ability to solve the formidable problem of entropy and information estimation.
Dr. Ilya Nemenman
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. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as 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 refereed through a 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.
- entropy estimation
- information estimation
- maximum entropy models
- information statistics
- mutual information
- kernel methods
- string matching
- Bayesian methods
- Shannon entropy
- Renyi entropy
- small data sets
Entropy 2013, 15(1), 80-112; doi:10.3390/e15010080
Received: 29 October 2012; in revised form: 7 December 2012 / Accepted: 21 December 2012 / Published: 27 December 2012| Download PDF Full-text (350 KB)
Article: Minimum Mutual Information and Non-Gaussianity through the Maximum Entropy Method: Estimation from Finite Samples
Entropy 2013, 15(3), 721-752; doi:10.3390/e15030721
Received: 8 November 2012; in revised form: 15 February 2013 / Accepted: 19 February 2013 / Published: 25 February 2013| Download PDF Full-text (637 KB)
Entropy 2013, 15(5), 1609-1623; doi:10.3390/e15051609
Received: 19 December 2012; in revised form: 25 April 2013 / Accepted: 28 April 2013 / Published: 6 May 2013| Download PDF Full-text (620 KB)
Entropy 2013, 15(5), 1690-1704; doi:10.3390/e15051690
Received: 1 February 2013; in revised form: 18 April 2013 / Accepted: 28 April 2013 / Published: 8 May 2013| Download PDF Full-text (740 KB)
Entropy 2013, 15(5), 1738-1755; doi:10.3390/e15051738
Received: 16 February 2013; in revised form: 24 April 2013 / Accepted: 2 May 2013 / Published: 10 May 2013| Download PDF Full-text (1877 KB)
Entropy 2013, 15(6), 1999-2011; doi:10.3390/e15061999
Received: 20 March 2013; in revised form: 8 May 2013 / Accepted: 17 May 2013 / Published: 23 May 2013| Download PDF Full-text (409 KB)
Article: Bootstrap Methods for the Empirical Study of Decision-Making and Information Flows in Social Systems
Entropy 2013, 15(6), 2246-2276; doi:10.3390/e15062246
Received: 15 March 2013; in revised form: 21 May 2013 / Accepted: 30 May 2013 / Published: 5 June 2013| Download PDF Full-text (461 KB)
Last update: 2 August 2012