Probability Modeling and Statistical Inferences in Medical Research
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematical Biology".
Deadline for manuscript submissions: 20 April 2025 | Viewed by 135
Special Issue Editor
Interests: cancer screening probability modeling; bayesian inference; statistical decision theory; data-driven statistical methods; applications in medical research
Special Issue Information
Dear Colleagues,
As more and more mathematicians and statisticians work in the field of medical research, more probability and statistical methods are developed in this area. These methods are usually driven by special types of data encountered in patients and hospital settings. The application of probability modeling and statistical inference is becoming increasingly critical in medical research. This Special Issue of Mathematics focuses on the latest developments and applications in these areas, addressing the growing complexity of healthcare data and the need for robust analytical methods. Probability modeling provides essential tools for understanding the stochastic nature of biological processes and patient behavior, while statistical inference ensures that conclusions drawn from data are valid and reliable.
We welcome submissions that demonstrate innovative applications and theoretical advancements in medical research. Topics of interest include Bayesian methods, survival analysis, epidemiological modeling, genomic data analysis, health economics, and the integration of machine learning with probabilistic approaches. This issue aims to bridge the gap between theoretical developments and practical applications, fostering interdisciplinary collaboration and driving innovation in medical research.
We look forward to contributions that highlight how advanced statistical techniques can enhance our understanding of complex medical phenomena and improve healthcare outcomes.
Prof. Dr. Dongfeng Wu
Guest Editor
Manuscript Submission Information
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Keywords
- probability modeling
- statistical modeling
- Bayesian analysis
- cancer screening
- medical applications
- survival analysis
- epidemiological modeling
- genomic data analysis
- health economics
- machine learning in medicine.
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