This concisely written book is a rigorous and self-contained introduction to the theory of continuous-time stochastic processes. A balance of theory and applications, the work features concrete examples of modeling real-world problems from biology, medicine, industrial applications, finance, and insurance using stochastic methods. No previous knowledge of stochastic processes is required.
Key topics covered include:
* Interacting particles and agent-based models: from polymers to ants
* Population dynamics: from birth and death processes to epidemics
* Financial market models: the non-arbitrage principle
* Contingent claim valuation models: the risk-neutral valuation theory
* Risk analysis in insurance
An Introduction to Continuous-Time Stochastic Processes will be of interest to a broad audience of students, pure and applied mathematicians, and researchers or practitioners in mathematical finance, biomathematics, biotechnology, and engineering. Suitable as a textbook for graduate or advanced undergraduate courses, the work may also be used for self-study or as a reference. Prerequisites include knowledge of calculus and some analysis; exposure to probability would be helpful but not required since the necessary fundamentals of measure and integration are provided.
Key topics covered include:
* Interacting particles and agent-based models: from polymers to ants
* Population dynamics: from birth and death processes to epidemics
* Financial market models: the non-arbitrage principle
* Contingent claim valuation models: the risk-neutral valuation theory
* Risk analysis in insurance
An Introduction to Continuous-Time Stochastic Processes will be of interest to a broad audience of students, pure and applied mathematicians, and researchers or practitioners in mathematical finance, biomathematics, biotechnology, and engineering. Suitable as a textbook for graduate or advanced undergraduate courses, the work may also be used for self-study or as a reference. Prerequisites include knowledge of calculus and some analysis; exposure to probability would be helpful but not required since the necessary fundamentals of measure and integration are provided.
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"Exercises are provided at the end of each chapter; the difficulty ranges from basic applications to more advanced ideas ... . Overall this book is a nice way to get into the basics of stochastic processes for someone working in a different field. It is quite reasonable that this could serve as either a main textbook or secondary reference for a graduate course. Sufficient details on each topic are provided by the authors, which makes this possible." (Eric Stachura, MAA Reviews, January 30, 2022)