Special Issue "Advance Methods for the Quantification of Correlations and Causal Relations between Processes"
Deadline for manuscript submissions: 31 July 2020.
Interests: nuclear fusion; entropy; information theory; machine learning; evolutionary computation; tomography; image processing
Interests: computed tomography; imagine processing; time series analysis, complex networks, data mining, Monte Carlo simulations
Interests: Plasma diagnostics; Inverse problems; Data mining; Time series analysis; Genetic programming
Two of the most relevant characteristics of modern societies are their complexity and the huge amounts of data that they produce. Unfortunately, in the investigation of complex systems, large datasets can become a liability, instead of an asset, if they are not analysed with adequate tools. One of the first steps in the formulation of scientific models and theories is certainly the assessment of the correlations between the quantities potentially involved. More advanced is the goal of determining their actual causal relations and relative strengths. In various domains, performing experiments and interventions to establish direct causal relationships could be unethical, extremely expensive, or even impossible. In the last few years, many efforts have been made to improve the techniques and methodologies for identifying and quantifying the correlations and the causal influences between processes based on time-series and cross-sectional data; they range from causal networks to phase space reconstructions and information-theoretic tools. For practical applications, the limited number of observations and the noise that inherently accompanies the measurements represent additional challenges.
This Special Issue aims to collect papers that describe new solutions for the above-mentioned problems. The contributions can be based on (but not limited to) the following fields:
- Information Theory;
- Network Theory;
- Statistical Inference;
- Machine Learning;
- Neural Computation;
- Genetic Programing.
Theoretical approaches as well as practical applications are welcome.
Prof. Dr. Andrea Murari
Dr. Teddy Craciunescu
Dr. Michela Gelfusa
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 papers will be 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 1600 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.