Recent Advances in Statistical Modeling and Simulations with Applications II

Dear Colleagues,

Every application is the result of a simulation process, which involves the use of advanced statistical techniques and is one of the most important aspects of data processing and analysis. The main purpose of statistical modeling is to simulate natural complex phenomena—both for analysis and to predict future processes. Therefore, the objective of any simulation is to identify the optimal or satisfactory solution to a problem through the operation of a real system. Often, failure to meet the above objectives is due to the complexity of the problem subjected to dig data processes.

Developing, supporting, and using simulation models requires statistical models in the form of statistical distributions. The important role of statistics is evident in simulations of reality, which are more realistic when the various variables and parameters are stochastic in nature. Statistical techniques are widely used to evaluate and predict variables, especially those used for big data, examples of which include medical images or general images, such as machine learning and neural networks.

Topics of interest for this Special Issue include, but are not limited to, the following:

• Big data;
• Machine learning;
• Neural networks;
• Image analysis;
• Medical imaging;
• Bayesian statistics;
• Applied statistics;
• Medical statistics;
• Ecology;
• Environmental sciences.

Dr. Stelios Zimeras
Guest Editor

Manuscript Submission Information

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• big data
• machine learning
• neural networks
• image analysis
• medical imaging
• Bayesian statistics
• applied statistics