Particle filters for state-space models with the presence of unknown static parameters

By Geir Storvik. IEEE Transactions on Signal Processing, 2002

490 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/289129304c544a0820e2bcee7ca2079d1b09f401

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Citations in the corpus

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].”
Particle methods for change detection, system identification, and control (2004, methodology): “Storvik’s Method. Storvik [ 52 ] has recently proposed an alternative method.”
Practical options for selecting data-driven or physics-based prognostics algorithms with reviews (2015, background): “On the other hand, BM is not practical for such a case because of tremendous computational costs caused by propagating damage for every cycle with thousands of samples separately [114,131,132].”
Dynamic Linear Models with R (2009, background): “Gilks and Berzuini (2001) and Storvik (2002) propose interesting alternative algorithms.”
An overview of sequential Monte Carlo methods for parameter estimation in general state-space models (2009, methodology): “Similar strategies were adopted in [21] and [46].”
Plug-and-play inference for disease dynamics: measles in large and small populations as a case study (2009, methodology): “In contrast, the approximations developed by Liu & West (2001) are not statistically efficient, and lead to additional bias and variance in the resulting parameter estimates (Storvik 2002; Newman et al. 2008).”
SMC2: an efficient algorithm for sequential analysis of state space models (2011, methodology): “…on-line algorithm may be obtained by extending SOPF so as to include MCMC updates of the θ -component, see Gilks and Berzuini (2001), Fearnhead (2002), Storvik (2002), and also the more recent work of Carvalho et al. (2010), but numerical evidence seems to indicate these algorithms degenerate as…”
Particle Markov chain Monte Carlo methods (2010, methodology): “This is the main reason behind the well-known difficulty of approximating p.θ, x1:T |y1:T / with SMC algorithms; see Andrieu et al. (1999), Fearnhead (2002) and Storvik (2002), for example.”
An Adaptive Sequential Monte Carlo Sampler (2010, methodology): “Such methods introduce steps into the algorithm to allow the support of the sample of parameter values to change over time, for example by using ideas from kernel density estimation (Liu and West, 2001), or MCMC moves (Gilks and Berzuini, 1999; Storvik, 2002; Fearnhead, 2002).”
Curse-of-dimensionality revisited: Collapse of the particle filter in very large scale systems (2008, background): “Another approach may be to condition the draws on lower-dimensional suﬃcient statistics ([5, 24]).”
An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo (2007, background): “Other more sophisticated examples of this approach are discussed in [103], [104], [105].”