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Table of Contents:

Chapter 1: Introduction
1.1 Background of the Study
1.2 Problem Statement
1.3 Research Questions
1.4 Significance of the Study
1.5 Structure of the Thesis

Chapter 2: Literature Review
2.1 Overview of Stochastic Processes
2.2 Poisson Processes
2.3 Markov Chains
2.4 Applications of Stochastic Processes in Various Fields

Chapter 3: Research Methodology
3.1 Data Collection Methods
3.2 Data Analysis Techniques
3.3 Research Design
3.4 Sampling Techniques

Chapter 4: Discussion of Findings
4.1 Analysis of Poisson Processes
4.2 Analysis of Markov Chains
4.3 Comparison of Results
4.4 Interpretation of Findings

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Recommendations for Future Research
5.3 Conclusion

Brief Overview of Thesis “Stochastic Processes: Poisson Processes and Markov Chains”:

The thesis “Stochastic Processes: Poisson Processes and Markov Chains” focuses on the study of two important types of stochastic processes – Poisson Processes and Markov Chains. Stochastic processes are random processes that evolve over time and are widely used in various fields such as economics, finance, engineering, and biology.

The thesis begins with an introduction that provides background information on stochastic processes and presents the problem statement and research questions. The literature review in Chapter 2 covers the basics of stochastic processes, Poisson Processes, and Markov Chains, and examines their applications in different fields.

Chapter 3 details the research methodology, including data collection methods, analysis techniques, research design, and sampling techniques. The discussion of findings in Chapter 4 analyzes the results of the study, comparing and interpreting the findings related to Poisson Processes and Markov Chains.

Finally, Chapter 5 presents the conclusion and summary of the thesis, summarizing the key findings, providing recommendations for future research, and concluding the study. This thesis aims to contribute to the understanding of stochastic processes, particularly Poisson Processes and Markov Chains, and their applications in various fields.

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