Sequential Monte Carlo in Practice

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

332 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4cf869c5b32dcae48226b4b842767fa93e308024

How researchers see this paper

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

Citations in the corpus

The Markov chain Monte Carlo revolution (2008, background): “For the very active area of particle filters and their many engineering applications (tracking, filtering), see [36].”
KLD-Sampling: Adaptive Particle Filters (2001, methodology): “Over the last years, particle filters have been applied with great success to a variety of state estimation problems (see [3] for a recent overview).”
Inferring High-Level Behavior from Low-Level Sensors (2003, methodology): “Particle filters provide a sample-based implementation of general Bayes filters [7].”
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).”
Gesture Recognition: A Survey (2007, methodology): “Particle-filtering-based tracking and its applications in gesture recognition systems became popular very recently [10]–[14].”
A two-stage ensemble Kalman filter for smooth data assimilation (2008, background): “For related filters relaxing or removing the Gaussian assumption, see Anderson and Anderson (1999), Bengtsson (2003), Doucet et al. (2001) , van Leeuwen (2003).”
Particle filters for state-space models with the presence of unknown static parameters (2002, background): “Today, many different versions of particle filters exist (see Doucet et al. [17]).”
Robust Part-Based Hand Gesture Recognition Using Kinect Sensor (2013, unknown): “particle filtering [19] and condensation algorithms [20].”
A modern Bayesian look at the multi-armed bandit (2010, methodology): “Such draws are available for a very wide class of models through Markov chain Monte Carlo and other sampling algorithms, which means that randomized probability matching can be applied with almost any family of reward distributions.”
GP-BayesFilters: Bayesian filtering using Gaussian process prediction and observation models (2008, unknown): “∂(k(x∗,xn)) ∂x∗[1]) .”