Recursive bayesian estimation using gaussian sums

By H. Sorenson, D. Alspach. 1971

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Citations in the corpus

The Gaussian Mixture Probability Hypothesis Density Filter (2006, background): “The Gaussian mixture PHD ﬁlter is similar to the Gaussian sum ﬁlter of [32] and [33] in the sense that they both propagate Gaussian mixtures in time.”
Under Review as a Conference Paper at Iclr 2016 Predicting Distributions with Linearizing Belief Networks (2015, background): “This work focuses maximum likelihood and we estimate the computationally expensive expectation from The model p(y|x) is a mixture of Gaussians and can therefore approximate any conditional distribution (Sorenson & Alspach, 1971).”
Bayesian methods for neural networks (2000, methodology): “A natural progression on sequential Gaussian approximation is to employ a mixture of Gaussian densities (Kadirkamanathan and Kadirkamanathan, 1995; Li and BarShalom, 1994; Sorenson and Alspach, 1971).”
Neural-Network-Based Fuzzy Logic Control and Decision System (1991, background): “Also it has been shown that Gaussian sum density estimator can approximate any probability density function to any desired degree of accuracy [ 36 ].”
Distributional assumptions of growth mixture models: implications for overextraction of latent trajectory classes. (2003, methodology): “For this reason, mixtures of normal distributions are often used to provide an approximation to complex or intractable distributions (Escobar & West, 1995; Ferguson, 1983; Roeder & Wasserman, 1997; Sorenson & Alspach, 1971; for a review, see Everitt & Hand, 1981, pp. 118–124; McLachlan & Peel, 200”
Bayesian Compressive Sensing Via Belief Propagation (2008, background): “Although BP has not been proved to converge, for graphs with few loops it often offers a good approximation to the solution to the MAP inference problem.”
State estimation for legged robots on unstable and slippery terrain (2013, background): “Others investigated the use of non-Gaussian distributions which are less susceptible to outliers [20, 23].”
Issues, progress and new results in robust adaptive control (2006, methodology): “We also remark that a similar adaptive estimation architecture, the so-called \Sum of Gaussians" method, utilizing a bank of extended Kalman fllters, was used for nonlinear flltering problems [57, 58 , 59].”
Gaussian sum particle filtering (2003, background): “Several ad hocprocedures have been suggested in [11] to reduce the number of mixands, but they may become inadequate in many practical problems and, moreover, difficult to implement.”
Sequential Monte Carlo Methods to Train Neural Network Models (2000, methodology): “A natural progression on sequential gaussian approximation is to employ a mixture of gaussian densities (Kadirkamanathan & Kadirkamanathan, 1995; Li & Bar-Shalom, 1994; Sorenson & Alspach, 1971).”
Active Contours (2000, methodology): “Additive Gaussian mixtures as used for representation of non-Gaussian densities in pattern recognition theory (Duda and Hart, 1973; Bishop, 1995) can be used in temporal filtering by dynamically re-weighting the mixture (Sorenson and Alspach, 1971).”