70 Years of Maximum Entropy—from Jaynes’ Foundations to Modern Inference and Data Driven Science
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".
Deadline for manuscript submissions: 30 June 2027 | Viewed by 411
Editors
Interests: Bayesian inference; inverse problems; information and maximum entropy; knowledge extraction; signal and image processing; computer vision; machine learning; neural network and AI; physics-informed NN; quantum computation
Special Issues, Collections and Topics in MDPI journals
Interests: probability theory; Bayesian inference; machine learning; information geometry; differential geometry; nuclear fusion; plasma physics; plasma turbulence; continuum mechanics; statistical mechanics
Special Issues, Collections and Topics in MDPI journals
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
Interests: Bayesian inference; Markov chain Monte Carlo; computational methods; astrostatistics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Seventy years ago, Edwin T. Jaynes introduced the Maximum Entropy principle in two landmark papers published in Physical Review in 1957: 'Information Theory and Statistical Mechanics' and 'Information Theory and Statistical Mechanics II'. In these seminal works, Jaynes proposed that statistical inference under incomplete information should be performed by selecting the probability distribution that maximizes entropy subject to known constraints. This perspective established a deep connection between statistical physics, probability theory, and information theory.
Over the past decades, the Maximum Entropy framework has become a unifying methodology across a wide range of scientific disciplines. Applications now include statistical physics, Bayesian inference, inverse problems, signal and image processing, machine learning, complex networks, biology, neuroscience, ecology, natural language processing, and financial modeling. The MaxEnt approach provides a principled framework for building models from incomplete or uncertain data while incorporating known constraints.
This Special Issue celebrates the 70‑year anniversary of Jaynes’ foundational work and aims to highlight current theoretical advances, computational algorithms, and impact-full applications of Maximum Entropy methods. Topics of interest include, but are not limited to, the following:
- Foundations of the Maximum Entropy principle;
- Maximum relative entropy and cross‑entropy methods;
- Maximum Entropy and Bayesian inference;
- Maximum caliber and dynamical inference;
- Statistical physics and non-equilibrium entropy production;
- Inverse problems and regularization;
- Signal processing and spectral estimation;
- Image reconstruction and computational imaging;
- Machine learning using MaxEnt models;
- Network inference and graphical models;
- Systems biology and neuroscience applications;
- Ecology and biodiversity modeling;
- Natural language processing;
- Financial modeling and uncertainty quantification;
- Information geometry and generalized entropy measures.
Dr. Ali Mohammad-Djafari
Prof. Dr. Geert Verdoolaege
Prof. Dr. Kevin H. Knuth
Dr. Brendon J. Brewer
Guest Editors
Manuscript Submission Information
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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-anonymized 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
- maximum entropy
- Bayesian inference
- statistical physics
- information geometry
- inverse problems
- machine learning
- quantum information
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