An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo

By Olivier Capp'e, Simon J. Godsill, Eric Moulines. 2007

1,089 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

Citations in the corpus

A Tutorial on Particle Filtering and Smoothing: Fifteen years later (2008, background): “As a result of the popularity of particle methods, a few tutorials have already been published on the subject [3] [8] [18] [29].”
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.”
Efficient Sequential Monte Carlo With Multiple Proposals and Control Variates (2016, background): “When observations have outliers or are very informative (with small variance), the number of particles after propagation in the high posterior likelihood area will be small, leading to particle degeneracy and therefore poor algorithm performance (Capp´e et al., 2007).”
Cubature Kalman Filters (2009, neutral): “For example, the point-mass filter using adaptive grids [13], the Gaussian mixture filter [14], and particle filters using Monte Carlo integrations with the importance sampling [15], [16] fall under this second category.”
A Tutorial on Bernoulli Filters: Theory, Implementation and Applications (2013, background): “The topic of parameter calibration for the Bernoulli filter remains unexplored until now, however, useful insights can be found in the literature on particle filters for parameter estimation of dynamic systems [7], [88], [89].”
Towards Automatic Model Comparison: An Adaptive Sequential Monte Carlo Approach (2013, methodology): “They have been primarily used as “particle ﬁl-ters” to solve optimal ﬁltering problems; see, for example, Capp´e et al. (2007); Doucet and Johansen (2011) for recent reviews.”
Diffusion of Lexical Change in Social Media (2012, background): “Our forward pass is a standard bootstrap ﬁlter [31]: by setting the proposal distribution q ( η i,r,t | η i,r,t − 1 ) equal to the transition distribution P ( η i,r,t | η i,t − 1 ; A , Γ ) , the forward particle weights are equal to the recursive product of the emission likelihoods, ( We…”
Statistical Inference for Partially Observed Markov Processes via the R Package pomp (2015, methodology): “Many variations and elaborations to SMC have been proposed; these may improve numerical performance in appropriate situations (Cappé et al. 2007) but typically”
A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies (2022, background): “28 gives the pseudo-code of a generic particle filter (Hu et al., 2018; Cappé et al., 2007).”
Online Sparse Multi-Output Gaussian Process Regression and Learning (2019, background): “3It is worthwhile to point out that the outputted θt is not necessarily the maximum a posteriori (MAP) estimate of the model hyperparameter vector θ [63], [64].”
Resampling Methods for Particle Filtering: Classification, implementation, and strategies (2015, background): “The popularity of PF has also spurred the publication of several review articles [2]–[6].”
Discrete-Time Nonlinear Filtering Algorithms Using Gauss–Hermite Quadrature (2007, methodology): “The SMC method consists of a large number of variants; many more are being constantly proposed in an attempt to improve both the statistical and computational efficiency (see [12], [ 13 ], [16], and the references therein).”