Switching Kalman Filters

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A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning (2017, background): “[20] introduces recurrent switching linear dynamical systems, that combine deep learning techniques and switching Kalman filters [22] to model low-dimensional time series.”
Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems (2017, background): “…dynamical systems (SLDS) [Ackerson and Fu, 1970, Chang and Athans, 1978, Hamilton, 1990, Bar-Shalom and Li, 1993, Ghahramani and Hinton, 1996, Murphy, 1998, Fox et al., 2009] by allowing the discrete switches to depend on the continuous latent state and exogenous inputs through a logistic…”
A Discontinuous Extended Kalman Filter for non-smooth dynamic problems (2017, methodology): “The suggested DEKF method can also be related to switching Kalman Filters ([30]) for which the the effect of choosing an estimated model M̂i has been investigated in greater depth.”
An algebraic geometric approach to the identification of a class of linear hybrid systems (2003, background): “Related work has also appeared in the machine learning community (see [6, 7, 8, 12, 13, 14] and references therein).”
Dynamical Variational Autoencoders: A Comprehensive Review (2020, methodology): “This model can be seen as an extension of the Switching Kalman Filter (Murphy, 1998; Fox et al., 2011) to a DNN-based observation model; see also (Linderman et al.”