Editors: Sequential Monte Carlo Methods in Practice

By Arnaud Doucet, J. F. G. D. Freitas, Neil J. Gordon. 2001

1,123 citations recorded by the source corpus.

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Citations in the corpus

Filtering with State-Observation Examples via Kernel Monte Carlo Filter (2013, methodology): “If the statistical models are more complex but still explicitly known, particle filters (Doucet et al., 2001) provide a strong solution for filtering.”
FastSLAM 2.0: an improved particle filtering algorithm for simultaneous localization and mapping that provably converges (2003, methodology): “It even holds if all features are arranged in a large loop, a situation often thought of as the worst case for SLAM problems [8]. 6 Experimental Results”
The vSLAM Algorithm for Robust Localization and Mapping (2005, methodology): “By also using a localization scheme with a particle filter [ 2 ] and an adaptive mixed proposal distribution [3], [5], vSLAM offers a breakthrough SLAM algorithm that allows navigation with good accuracy in various real-world environments.”
Efficient Multiscale Sampling from Products of Gaussian Mixtures (2003, methodology): “Importance sampling is a Monte Carlo method for approximately sampling from (or computing expectations of) an intractable distribution p(x), using a proposal distribution q(x) for which sampling is feasible [ 7 ].”
Fast multiple object tracking via a hierarchical particle filter (2005, methodology): “In the case of the bootstrap filter [ 10 ], q(xt|x1:t−1,z1:t )= p(xt|xt−1) and the weights become the observation likelihood p(zt|xt).”
Data fusion for visual tracking with particles (2004, unknown): “See examples in [7], [13], [ 14 ], [26], [28], [41], [43], [45], [46], [48], [49].”
Adaptive methods for sequential importance sampling with application to state space models (2008, methodology): “…of the modes of the unnormalised optimal sampling distribution by means of some optimisation algorithm, and the fitting of over-dispersed student’s t-distributions around these modes; see for example Shephard and Pitt (1997), Doucet et al. (2001), and Liu (2004) (and the references therein).”
Towards robotic assistants in nursing homes: Challenges and results (2003, background): “where denotes the sequence of robot poses (the path) up to time . If was known, estimating this posterior would be a high-dimensional estimation problem, with complexity cubic in for Kalman filters (2), or exponential in with particle filters ( 8 ).”