Special Issue "Bayesian Inference and Information Theory"
Deadline for manuscript submissions: closed (30 April 2019)
Prof. Dr. Kevin H. Knuth
Department of Physics, University at Albany, 1400 Washington Avenue, Albany, NY 12222, USA
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Fax: +1 518 442 5260
Interests: entropy; probability theory; Bayesian; foundational issues; lattice theory; data analysis; maxent; machine learning; robotics; information theory; entropy-based experimental design
In Bayesian inference, probabilities describe plausibility, or the degree to which one statement implies another. In a similar manner, the entropies of information theory describe relevance, or the degree to which resolving one question would resolve another. However, this latter understanding is relatively undeveloped and is not often used in practical Bayesian data analysis. In this Special Issue we invite contributions to the area of Bayesian inference and information theory. The following suggested subtopics are of particular interest:
- Foundations of Bayesian inference and information theory
- Applications of Bayesian inference involving well-motivated uses of information theoretic concepts
- Bayesian experimental design
- Maximum entropy and choice of prior distributions
We look forward to receiving your contributions.
Prof. Dr. Kevin H. Knuth
Dr. Brendon J. Brewer
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.
- Bayesian inference;
- Information theory;
- Bayesian data analysis;
- Maximum entropy;
- Prior distributions;
- Kullback-Leibler divergence;