Sequential Monte Carlo (SMC) methods are simulation-based techniques for computing posterior distributions in Bayesian inference. They are flexible, easy to implement, and applicable in general settings. Unlike grid-based filters, which are computationally expensive and difficult to implement, SMC methods are efficient and suitable for high-dimensional problems. The increasing availability of computational power and recent advances in statistics, engineering, and probability have led to significant progress in SMC methods. These methods have been widely applied in various fields, including finance, target tracking, navigation, computer vision, machine learning, and robotics. This book aims to introduce SMC methods to a broader audience, presenting the latest algorithmic and theoretical developments and demonstrating their use in complex applications. The book is divided into three parts: the first part provides a detailed theoretical treatment of SMC algorithms, the second part discusses methods to improve the efficiency of the basic SMC algorithm, and the third part covers various applications in different fields. Each chapter is self-contained and can be read independently. However, due to space constraints, not all leading researchers in the field could be included, and some theoretical and practical issues were not addressed in depth. The book serves as a comprehensive resource for understanding SMC methods and their applications.Sequential Monte Carlo (SMC) methods are simulation-based techniques for computing posterior distributions in Bayesian inference. They are flexible, easy to implement, and applicable in general settings. Unlike grid-based filters, which are computationally expensive and difficult to implement, SMC methods are efficient and suitable for high-dimensional problems. The increasing availability of computational power and recent advances in statistics, engineering, and probability have led to significant progress in SMC methods. These methods have been widely applied in various fields, including finance, target tracking, navigation, computer vision, machine learning, and robotics. This book aims to introduce SMC methods to a broader audience, presenting the latest algorithmic and theoretical developments and demonstrating their use in complex applications. The book is divided into three parts: the first part provides a detailed theoretical treatment of SMC algorithms, the second part discusses methods to improve the efficiency of the basic SMC algorithm, and the third part covers various applications in different fields. Each chapter is self-contained and can be read independently. However, due to space constraints, not all leading researchers in the field could be included, and some theoretical and practical issues were not addressed in depth. The book serves as a comprehensive resource for understanding SMC methods and their applications.