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.
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.

Modelling Stochastic Uncertainties: From Monte Carlo Simulations to Game Theory
332
Modelling Stochastic Uncertainties: From Monte Carlo Simulations to Game Theory
332Product Details
ISBN-13: | 9783111584706 |
---|---|
Publisher: | De Gruyter |
Publication date: | 11/18/2024 |
Series: | De Gruyter Textbook |
Pages: | 332 |
Product dimensions: | 6.69(w) x 9.45(h) x (d) |