An efficient stochastic approximation EM algorithm using conditional particle filters

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Causal Discovery from Temporally Aggregated Time Series (2017, methodology): “…non-Gaussian state-space model is intractable, we estimate the model parameters using the particle stochastic approximation EM (PSAEM) algorithm (Lindsten, 2013; Svensson et al., 2014), which combines the efficient conditional particle filter with ancestor sampling (CPF-AS) (Lindsten et al.,…”
Identification of jump Markov linear models using particle filters (2014, methodology): “5 standard deviation (fields) H2 error for 7 runs of our RB-PSAEM using N = 3 particles (black) PSAEM [6] using N = 20 particles (blue) and PSEM [20] using N = 100 particles and M = 20 backward trajectories (red).”
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models (2019, methodology): “…algorithm (Delyon et al., 1999), combining with conditional particle ﬁlters with ancestor sampling (CPF-AS) in the E step (Lindsten et al., 2012; Lindsten, 2013), for model estimation. where B ( k,j ) t is the sampled j th particle of B t at the k th iteration, and h ( k,j ) t is the sampled…”