Novel approach to nonlinear/non-Gaussian Bayesian state estimation

By Neil J. Gordon, D. Salmond, Adrian F. M. Smith. 1993

Subfields: Probabilistic & Bayesian Methods

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In Probabilistic & Bayesian Methods

Citations in the corpus

Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering (2015, methodology): “Then we have: where ˆ k W k and ˆ (cid:15) t is the FW error reported at time t by the algorithm: We note that χ t ≈ 1 as we expect the errors on W k to go in either direction, and thus to cancel each other over time (though in the worst case it could grow exponentially in t ).”
Structured Pruning of Deep Convolutional Neural Networks (2015, background): “Secondly it directly performs on the input layer and is therefore more sensitive to pruning [7].”
Bayesian Analysis of DSGE Models (2006, background): “Gordon et al. (1993) and Kitagawa (1996) are early contributors to this literature.”
The scaled unscented transformation (2002, background): “When the transformation is nonlinear no general closed-form solutions exist [1] and many approximations have been proposed [2–6].”
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) to”
Bayesian assessment of the expected data impact on prediction confidence in optimal sampling design (2012, methodology): “[11] In this work, we propose an extension of the bootstrap filter (BF) [Gordon et al., 1993] toward optimal design, i.e., to the optimal planning of data acquisition, aimed at improved model confidence after conditioning a model on the data.”
Bayesian State-Space Modelling on High-Performance Hardware Using LibBi (2013, methodology): “The particle filter [31, 14] is an alternative that does not, and admits any SSM of the form (2).”
Mobile Robot Localisation and Mapping in Extensive Outdoor Environments (2002, background): “Sample impoverishment is countered in [60] by introducing “roughening”—adding Gaussian jitter to each resampled element x+k (i)—which seems to effectively eliminate sample dependence.”
Monte Carlo Smoothing for Nonlinear Time Series (2004, methodology): “ticle lter (Gordon et al. 1993) with N D 10,000 particles.”
Probabilistic recognition of human faces from video (2002, methodology): “A computationally efficient sequential importance sampling (SIS) algorithm is developed to estimate the posterior distribution.”
Smoothing algorithms for state–space models (2010, methodology): “Following reference [24] we used 500 particles over 50 time epochs.”
Monte Carlo methods for estimating, smoothing, and filtering one- and two-factor stochastic volatility models (2006, methodology): “One way to obtain these is by means of a particle filter (e.g., Gordon et al., 1993 or Pitt and Shephard, 1999).”
A new method for the nonlinear transformation of means and covariances in filters and estimators (2000, background): “1, we show the average magnitude of the state errors committed by each filter across a Monte Carlo simulation consisting of 50 runs.”
SMOOTH VARIABLE STRUCTURE FILTERING: THEORY AND APPLICATIONS (2011, background): “The PF may also be known as Monte Carlo filters [31], interacting particle approximations [74], bootstrap filters [75], condensation algorithms [76], and survival of the fittest methods [77], to name a few.”