Probability, Stochastic Processes and Optimization
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics".
Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 33925
Special Issue Editors
2. Institute of Mathematical Statistics and Applied Mathematics, 050711 Bucharest, Romania
Interests: stochastic processes; evolutionary algorithms; cellular automata
Interests: simulation methods; Monte Carlo method; statistical learning; data mining; artificial intelligence; biostatistics
Interests: markov and semi-Markov processes; hidden Markov and hidden semi-Markov processes; statistical inference for stochastic processes; parametric and nonparametric estimation; hypotheses testing; stochastic methods in reliability and survival analysis; biostatistics; stochastic methods for DNA modelling; entropy and divergence measures; model selection
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In applied research, Probability Theory is usually regarded as a distant, often neglected relative of Statistics. We try to rectify this misconception by publishing papers underlying new applications and original models for real-world problems, from either natural, computer or social sciences that are based on Probability and Stochastic Processes.
The suggested, yet not restrictive, topics include the following:
- Optimization;
- Cellular automata;
- Bayes analysis;
- Markov processes and related topics;
- Hidden Markov processes;
- Applications of stochastic processes in Economics, Finance, Social Sciences, etc.;
- Stochastic modeling;
- Measures of divergence and entropy;
- Clustering;
- Monte Carlo simulations.
Prof. Dr. Alexandru Agapie
Prof. Dr. Denis Enachescu
Dr. Vlad Stefan Barbu
Prof. Dr. Bogdan Iftimie
Guest Editors
Manuscript Submission Information
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Keywords
- Probabilistic optimization
- Evolutionary algorithms
- Stochastic modeling
- Probabilistic cellular automata
- Entropy
- Markov processes and related topics in social sciences
- Hidden Markov chains
- Markov chains for learning human behavior
- Stochastic processes and their applications in economics
- Clustering
- Monte Carlo methods
- Bayesian analysis
- Bayesian networks
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