Corpus ID: 202541210

A Closed-Form Filter for Binary Time Series

@article{Fasano2019ACF,
  title={A Closed-Form Filter for Binary Time Series},
  author={Augusto Fasano and Giovanni Rebaudo and Daniele Durante and Sonia Petrone},
  journal={arXiv: Methodology},
  year={2019}
}
Non-Gaussian state-space models arise in several applications. Within this framework, the binary time series setting is a source of constant interest due to its relevance in many studies. However, unlike Gaussian state-space models, where filtering, predictive and smoothing distributions are available in closed-form, binary state-space models require approximations or sequential Monte Carlo strategies for inference and prediction. This is due to the apparent absence of conjugacy between the… Expand

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