References

1. Ross, Sheldon M. (2020). Introduction to Probability Models (12th ed.). Cambridge: Academic Press. Explores probability models with applications in decision-making, emphasizing prediction and risk assessment.
2. Narendra Kumar and Anil Kumar “Performance for Mathematical Model of DNA Supercoil.” In the Bio-Science Research Bulletin, vol 22(2), pp79-87, 2007.( GALE/A199539280)
3. Narendra Kumar, B. Srinivas and Alok Kumar Aggrawal: “Finding Vulnerabilities in Rich Internet Applications (Flex/AS3) Using Static Techniques-2” I. J. Modern Education and Computer Science, 2012, 1, 33-39.(http://www.mecs-press.org/ DOI: 10.5815/ijmecs.2012.01.05)
4. Bedford, Tim, & Cooke, Roger M. (2001). Probabilistic Risk Analysis: Foundations and Methods. Cambridge: Cambridge University Press. Focuses on methodologies for quantifying uncertainty and assessing risks in decision contexts.
5. Kaplan, Stanley, & Garrick, B. John. (1981). "On the Quantitative Definition of Risk," Risk Analysis, 1(1), 11-27. Seminal paper defining risk quantitatively through probability and consequence analysis.
6. Gelman, Andrew, et al. (2013). Bayesian Data Analysis (3rd ed.). Boca Raton: CRC Press. Highlights Bayesian approaches to improve predictive modeling and manage uncertainty in data analysis.
7. Narendra Kumar, B. Srinivas and Alok Kumar Aggrawal: “Web Application Vulnerability Assessment” International Journal of Enterprise computing and Business Systems”, vol-1, 2011(https://www.atlantis-press.com/proceedings/cac2s-13/6377)
8. Megha Singla, Mohit Dua and Narendra Kumar: “CNS using restricted space algorithms for finding a shortest path”. International Journal of Engineering Trends and Technology, 2(1), 48-54, 2011.( https://ijettjournal.org/archive/ijett-v2i1p204)
9. McNeil, Alexander J., Frey, Rudiger, & Embrechts, Paul. (2015). Quantitative Risk Management: Concepts, Techniques, and Tools. Princeton: Princeton University Press. Examines financial risk through advanced probabilistic frameworks.
10. Wilks, Daniel S. (2011). Statistical Methods in the Atmospheric Sciences (3rd ed.). San Diego: Academic Press. Discusses probabilistic forecasting techniques in atmospheric sciences.
11. Taleb, Nassim Nicholas. (2007). The Black Swan: The Impact of the Highly Improbable. New York: Random House. Investigates the unpredictability of rare events and the limitations of traditional probability.
12. Jorion, Philippe. (2007). Value at Risk: The New Benchmark for Managing Financial Risk (3rd ed.). New York: McGraw-Hill. Addresses financial risk management through probabilistic modeling techniques.
13. Sherman, Michael. (2011). Spatial Statistics and Spatio-Temporal Data. New York: Wiley. Examines the role of probability in modeling spatial and temporal phenomena.
14. Spiegelhalter, David. (2019). The Art of Statistics: Learning from Data. London: Pelican Books. Explains the role of statistical probability in solving real-world problems.
15. Colombo, Massimiliano G., & Dawid, Philip. (2017). "Dynamic Probabilistic Systems in Risk Management," Decision Analysis Journal, 14(2), 85-102. Analyzes the application of dynamic probabilistic systems in risk management.
16. Smithson, Michael. (1989). Ignorance and Uncertainty: Emerging Paradigms. New York: Springer. Explores methods for handling uncertainty and gaps in knowledge through probabilistic approaches.