Sequential Monte Carlo Methods In Practice
Sequential Monte Carlo (SMC) methods, often known as particle filters, provide powerful computational tools for analyzing complex dynamic systems. They are particularly effective for problems involving non-linear state-space models and non-Gaussian noise, making them indispensable for robust state estimation, parameter inference, and tracking across various practical applications in engineering, finance, and robotics.