Markov Chain Models and Applications: Latest Advances and Prospects
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D1: Probability and Statistics".
Deadline for manuscript submissions: closed (20 March 2025) | Viewed by 1457
Special Issue Editor
Special Issue Information
Dear Colleagues,
Markov chains are ubiquitous stochastic processes that are of theoretical interest in their own right, but also used within statistical inference procedures and applied to many complex systems across applied sciences.
This Special Issue will host manuscripts pushing the frontiers of discrete and continuous-time Markov chains, covering problems of both theoretical and computational natures. Examples could include manuscripts related to the following themes:
- Markov chain Monte Carlo methods and diagnostics.
- Statistical inference and data science for and in Markov chains and processes.
- Sensitivity and uncertainty analysis in Markov chain networks.
- Applications of Markov chains to real-life problems, including, but not limited to, systems biology, ecology, epidemiology, and engineering.
Dr. Ben Swallow
Guest Editor
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly 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
- Markov chain Monte Carlo methods and diagnostics
- statistical inference and data science for and in Markov chains and processes
- sensitivity and uncertainty analysis in Markov chain networks
- applications of Markov chains to real-life problems, including, but not limited to, systems biology, ecology, epidemiology, and engineering
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