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This book delves into dynamic systems modeling, probability theory, stochastic processes, estimation theory, Kalman filters, and game theory. While many excellent books offer insights into these topics, our proposed book takes a distinctive approach, integrating these diverse subjects to address uncertainties and demonstrate their practical applications.
The author aims to cater to a broad spectrum of readers. The book features approximately 150 meticulously explained solved examples and numerous simulation programs, each with detailed explanations.
"Modelling Stochastic Uncertainties" provides a comprehensive understanding of uncertainties and their implications across various domains. Here is a brief exploration of the chapters:
Chapter 1: Introduces the book's philosophy and the manifestation of uncertainties.
Chapter 2: Lays the mathematical foundation, focusing on probability theory and stochastic processes, covering random variables, probability distributions, expectations, characteristic functions, and limits, along with various stochastic processes and their properties.
Chapter 3: Discusses managing uncertainty through deterministic and stochastic dynamic modeling techniques.
Chapter 4: Explores parameter estimation amid uncertainty, presenting key concepts of estimation theory.
Chapter 5: Focuses on Kalman filters for state estimation amid uncertain measurements and Gaussian additive noise.
Chapter 6: Examines how uncertainty influences decision-making in strategic interactions and conflict management.
Overall, the book provides a thorough understanding of uncertainties, from theoretical foundations to practical applications in dynamic systems modeling, estimation, and game theory.
Summary
This book delves into dynamic systems modeling, probability theory, stochastic processes, estimation theory, Kalman filters, and game theory. While many excellent books offer insights into these topics, our proposed book takes a distinctive approach, integrating these diverse subjects to address uncertainties and demonstrate their practical applications.
The author aims to cater to a broad spectrum of readers. The book features approximately 150 meticulously explained solved examples and numerous simulation programs, each with detailed explanations.
"Modelling Stochastic Uncertainties" provides a comprehensive understanding of uncertainties and their implications across various domains. Here is a brief exploration of the chapters:
Chapter 1: Introduces the book's philosophy and the manifestation of uncertainties.
Chapter 2: Lays the mathematical foundation, focusing on probability theory and stochastic processes, covering random variables, probability distributions, expectations, characteristic functions, and limits, along with various stochastic processes and their properties.
Chapter 3: Discusses managing uncertainty through deterministic and stochastic dynamic modeling techniques.
Chapter 4: Explores parameter estimation amid uncertainty, presenting key concepts of estimation theory.
Chapter 5: Focuses on Kalman filters for state estimation amid uncertain measurements and Gaussian additive noise.
Chapter 6: Examines how uncertainty influences decision-making in strategic interactions and conflict management.
Overall, the book provides a thorough understanding of uncertainties, from theoretical foundations to practical applications in dynamic systems modeling, estimation, and game theory.
