Summary
The sequential analysis of state-space models remains to this day the main application of Sequential Monte Carlo. The intent of this Chapter is to define informally state-space models, and discuss several typical examples of such models from different areas of Science.
However, we warn readers beforehand that we will need the mathematical machinery developed in the following chapters to define in sufficient generality state-space models, to develop recursions for filters and smoothers, and design a variety of simulation algorithms, including particle filters.
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Chopin, N., Papaspiliopoulos, O. (2020). Introduction to State-Space Models. In: An Introduction to Sequential Monte Carlo. Springer Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-030-47845-2_2
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