Applied Probability, Statistics and Operational Research
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics".
Deadline for manuscript submissions: 31 December 2024 | Viewed by 214
Special Issue Editors
Interests: the interface of applied probability and statistics, especially the modeling of heavy-tailed phenomena in complex networks
Interests: Bayesian asymptotics; Bayesian nonparametrics; big data analysis; geometry and statistics; network analysis; probabilistic graphical models; topological data analysis
Interests: financial risk management; portfolio optimization; insurance risk classification and pricing, and web search traffic forecasting
Special Issue Information
Dear Colleagues,
In the era of big data, a significant amount of research efforts has been devoted to the inter-discipline of applied probability, statistics, and operations research, with new methodologies and theories developed to resolve real-life problems. The focus of this Special Issue is oriented toward, but not limited to, the asymptotic analysis and optimization of probabilistic models, the development of novel statistical inference methods, as well as the data-driven decision-making process. Applications of new analytical tools to interesting real-life scenarios are also welcome. This Special Issue aims to provide a platform for researchers in the three disciplines to share their recent advances related to the fields and discuss insights into future research opportunities and challenges.
Dr. Tiandong Wang
Dr. Lizhen Lin
Dr. Zifeng Zhao
Guest Editors
Manuscript Submission Information
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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
- applied probability
- statistical inference
- stochastic processes and applications
- stochastic control
- optimization
- statistical learning
- fuzzy methods
