An overview of sequential Monte Carlo methods for parameter estimation in general state-space models

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Learning deep dynamical models from image pixels (2014, methodology): “ntication for the last decades [10, 17]. In recent years, sequential Monte Carlo (SMC) methods have received attention for identifying non-linear state-space models [15], see also the recent surveys [6]. While methods based on SMC are powerful, they are also computationally expensive. Learning non-l”
Statistical inference for dynamical systems: A review (2012, unknown): “For an incomplete list of works concerning SMC and particle filtering, as well as their adaptations to parameter estimation, see [11, 24, 26, 32, 33, 34, 44, 60, 73, 85, 90, 100, 115].”
SMC2: an efficient algorithm for sequential analysis of state space models (2011, methodology): “002), Storvik (2002), and also the more recent work of Carvalho et al. (2010), but numerical evidence seems to indicate these algorithms degenerate as well, albeit possibly at a slower rate; see e.g. Doucet et al. (2009). On the other hand, SMC2 is a generic approach which does not require such a sp”