State Space Models
State Space Models (SSMs) are a class of probabilistic models used to describe the evolution of a system over time. They consist of two components: a state equation that describes how the system evolves over time, and an observation equation that relates the system's state to the observed data. SSMs are widely used in time series analysis, signal processing, and control theory. They are particularly useful in situations where the underlying system is complex and difficult to model directly, but where there is some understanding of how the system behaves over time. SSMs can be used to estimate the state of the system at any point in time, and to make predictions about future states. They can also be used to filter noisy data, by combining the observed data with the model's predictions to produce a more accurate estimate of the system's state.
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