Sequential Monte Carlo Methods in Practice

By Arnaud Doucet, Nando de Freitas, Neil J. Gordon. 2001

3,673 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence, Probabilistic & Bayesian Methods

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Citations in the corpus

P3O: Policy-on Policy-off Policy Optimization (2019, neutral): “, 2009) or in particle filtering (Smith, 2013).”
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks (2000, methodology): “Rao-Blackwellised particle filters (RBPF) have been applied in specific contexts such as mixtures of Gaussians (Akashi and Kumamoto 1977, Doucet 1998, Doucet, Godsill and Andrieu 2000 ), fixed parameter estimation (Kong, Liu and Wong 1994), HMMs (Doucet 1998, Doucet, Godsill and Andrieu 2000) and D”
Generalized fiducial inference for normal linear mixed models (2012, neutral): “The Annals of Statistics 2012, Vol. 40, No. 4, 2102–2127 DOI: 10.1214/12-AOS1030 © Institute of Mathematical Statistics, 2012”
The Generalized Cross Entropy Method, with Applications to Probability Density Estimation (2011, background): “In the particle filter context (see Doucet et al. 2001 page 296 ) the set {λi} n=0 is the set of Sampling Importance Resampling (SIR) weights.”
Filtering in Finance (2003, background): “More recently, Pitt & Shephard [10] suggested the use of Auxiliary Particle Filters to overcome some of these difficulties.”
Meta-Q-Learning (2019, background): “tor (10). It is not possible to compute the ESS without knowing both densitiesd q(x) and p(x) but there are many heuristics for estimating it. A popular one in the Monte Carlo literature (Kong, 1992; Smith, 2013; Elvira et al., 2018) is ESSd= 1 m ( P m k=1 (x k)) 2 P m k=1 (x k)2 2[0;1] (13) where X”
Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters (2007, methodology): “[7], we obtain a recursive formulation to compute the importance weights by restricting the proposal π to fulfill the following assumption”
Markov Chain Monte Carlo Data Association for Multi-Target Tracking (2009, methodology): “5 Recently, JPDA has also been applied to nonlinear problems using a particle filter[37].Noticethatwhenthedynamicsandmeasurementmodelarenonlinear or the noise processes are non-Gaussian, the posterior distribution can be approximated using techniques such as linearization, unscented filtering [38], ”
Plug-and-play inference for disease dynamics: measles in large and small populations as a case study (2009, methodology): “Both Bayesian (Liu & West 2001; Toni et al. 2009) and frequentist (Ionides et al. 2006; Poyiadjis et al. 2006) approaches to plug-and-play likelihood-based inference via sequential Monte Carlo have been proposed.”
A sequential particle filter method for static models (2002, methodology): “These methods are usually referred to as ‘particle filter methods’ (Doucet et al., 2001, Ch. 1).”