Identification of jump Markov linear models using particle filters
By Andreas Svensson, Thomas Bo Schön, Fredrik Lindsten. 2014
30 citations recorded by the source corpus.
Jump Markov linear models consists of a finite number of linear state space models and a discrete variable encoding the jumps (or switches) between the different linear models. Identifying jump Markov linear models makes for a challenging problem lacking an analytical solution. We derive a new expectation maximization (EM) type algorithm that produce maximum likelihood estimates of the model parameters. Our development hinges upon recent progress in combining particle filters with Markov chain Monte Carlo methods in solving the nonlinear state smoothing problem inherent in the EM formulation. Key to our development is that we exploit a conditionally linear Gaussian substructure in the model, allowing for an efficient algorithm.
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- Bernhard Schölkopf
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Citations in the corpus
- Causal Discovery from Temporally Aggregated Time Series (2017, methodology): “Following (Lindsten, 2013; Svensson et al., 2014), we construct the Markov kernel using Rao-Blackwellized conditional particle filter with ancestor sampling (RB-CPF-AS) (Lindsten et al., 2014), which was originally proposed for Gibbs sampling.”