Following a moving target—Monte Carlo inference for dynamic Bayesian models

By Walter R. Gilks, C. Berzuini. 2001

865 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Oceanography

View Paper: https://www.semanticscholar.org/paper/446ee311cbefdf5a07d2b4a409c9d4229cc59739

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Citations in the corpus

Dynamic Covariance Models for Multivariate Financial Time Series (2013, methodology): “We analyzed several particle ﬁltering methods, including Resample-Move (Gilks & Berzuini, 2001), Regularized Sequential Importance Sampling (Musso et al., 2001), Regularized Sequential Importance Resampling (Gordon et al., 1993), and Regularized Auxiliary Particle Filter (RAPF) (Liu & West, 1999)…”
Importance Sampling Squared for Bayesian Inference in Latent Variable Models (2013, methodology): “IfpbN(y|θ)=p(y|θ),theabovealgorithmisaspecialcase oftheSMC sampler inDel Moral et al. (2006) using the backward kernel (30) in their paper. It is also closely related to the resamplemove algorithm of Gilks and Berzuini (2001), except that they perform the resampling step in every iteration t. 3.1.1 ”
An Adaptive Sequential Monte Carlo Sampler (2010, methodology): “One step of an SMC algorithm can involve importance reweighting, resampling and moving the particles via an MCMC kernel (Gilks and Berzuini, 1999; Chopin, 2002).”
Sequential Monte Carlo methods for multitarget filtering with random finite sets (2005, methodology): “Carlo (MCMC) step can also be applied to increase particle diversity [ 14 ].”
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking (2002, methodology): “One such technique is the resample-move algorithm [19], which is not be described in detail in this paper.”
A survey of convergence results on particle filtering methods for practitioners (2002, background): “Remark 1: Recently, the introduction of Markov chain Monte Carlo steps in particle filtering algorithms has been suggested; see [18].”
An adaptive sequential Monte Carlo method for approximate Bayesian computation (2011, methodology): “…the backward Markov kernel an approximation of the optimal backward kernel (7) given by the reversal kernel Note that this choice of backward kernels was implicitly made in Chopin (2002), Gilks and Berzuini (2001) and explicitly in related algorithms Neal (2001) where no resampling step is used.”
Intelligent Particle Filter and Its Application to Fault Detection of Nonlinear System (2015, methodology): “In [15], Gilks and Berzuini proposed the resamplemove algorithm for mitigating the particle impoverishment based on the Markov chain Monte Carlo sampling.”
An Introduction to MCMC for Machine Learning (2004, background): “It is also possible to introduce MCMC steps of invariant distribution p(x0:t | y1:t ) on each particle (Andrieu, de Freitas, & Doucet, 1999; Gilks & Berzuini, 1998; MacEachern, Clyde, & Liu, 1999).”
SMC2: an efficient algorithm for sequential analysis of state space models (2011, positive): “…the existence of suﬃcient statistics), an 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…”
Beyond Gaussian Statistical Modeling in Geophysical Data Assimilation (2010, methodology): “The collapse of the weights could be avoided using Monte Carlo Markov chain (MCMC) techniques, based for instance on a Metropolis–Hastings selection algorithm (Gilks and Berzuini 2001).”
Monte Carlo Smoothing for Nonlinear Time Series (2004, methodology): “We have experimented with various types of Monte Carlo filter, adapted to our specific TVAR model, using the standard sequential importance sampling filter (Doucet et al. 2000; Gordon et al. 1993; Liu and Chen 1998), the auxiliary particle filter (APF) (Pitt and Shephard 1999a), and schemes that inc”
Bayesian statistical data assimilation for ecosystem models using Markov Chain Monte Carlo (2007, methodology): “However, these have not been widely applied except as part of more comprehensive SIR algorithms (Gilks and Berzuini, 2001; Lee and Chia, 2002; Dowd, 2006).”
Adapting the Sample Size in Particle Filters Through KLD-Sampling (2003, methodology): “Furthermore, our approach can be combined with any other method for improving the efficiency of particle filters (Gilks and Berzuini 2001; Pitt and Shephard 1999; Godsill and Clapp 2001; Thrun et al. 2001).”