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- W4253579551 abstract "This chapter discusses an effective Monte Carlo approach designed specifically for dealing with state space models (SSMs). It is referred to as the sequential Monte Carlo (SMC) method. SMC uses discrete random samples to represent the conditional distributions. This approach belongs to the class of density-based filtering approaches. SMC achieves estimation and optimization tasks by recursively generating Monte Carlo samples of the state variables of a system. The chapter presents a brief introduction to general Monte Carlo methods, and then establishes a general framework of SMC. It discusses several specific implementations for a wide range of nonlinear non-Gaussian SSMs. Monte Carlo methods are essentially a class of numerical methods to evaluate some integrals. They rely on random sampling, which is the key principle behind all statistical analyses, and use statistical methods to assess the estimation accuracy. Another important issue in using Monte Carlo methods is variance reduction." @default.
- W4253579551 created "2022-05-12" @default.
- W4253579551 date "2018-08-29" @default.
- W4253579551 modified "2023-09-24" @default.
- W4253579551 title "Sequential Monte Carlo" @default.
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- W4253579551 doi "https://doi.org/10.1002/9781119514312.ch8" @default.
- W4253579551 hasPublicationYear "2018" @default.
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