de Freitas, J. F. G., Niranjan, M., Gee, A. H. and Doucet, A. (2000). Sequential Monte Carlo methods to train neural network models, To appear in Neural Computation. Home/Search Document Not in Database Summary Related Articles Check |
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Numerical Integration by Cubature Formulae in Bayesian.. - van Wezel, Kosters (Correct)
....as they can be seen as single best guess and error bar . Unfortunately, approximating these integrals is extremely dicult. In the remainder we will compare two approximation methods: hybrid MCMC and cubature formulae combined with a transformation. For other approximation methods, see [4, 3]. 3 Hybrid Markov Chain Monte Carlo This method was introduced by Neal [5] We do not describe it in detail here. Essentially, the integrals (5) and (6) are approximated by an average of K values sampled according to the predictive distribution: f(xN 1 jw i ) 7) var[t N 1 ] ....
J. F. G. de Freitas, M. Niranjan, A. H. Gee, and A. Doucet. Sequential Monte Carlo methods to train neural network models. Neural Computation, 12(4):955{ 993, 2000.
FastSLAM 2.0: An Improved Particle Filtering Algorithm for.. - Montemerlo, Thrun (2003) (Correct)
....in the same manner as the EKF. As a result, the proposal distribution can be calculated in closed form. This extension parallels prior work by Doucet and colleagues, who proposed a similar modification for general particle filters [6] and Markov Chain Monte Carlo techniques for neural networks [4] . It is similar to the arc reversal technique proposed for particle filters applied to Bayes networks [10] and it is similar to recent work by van der Merwe [24] who uses an unscented filtering step [9] for generating proposal distributions that accommodate the measurement. While this ....
N. de Freitas, M. Niranjan, A. Gee, and A. Doucet. Sequential monte carlo methods to train neural network models. Neural Computation, 12(4), 2000.
Visual Contour Tracking Based on Particle Filters - Li, Zhang (2002) (Correct)
....in many cases an auxiliary tracker may not be obtained. MacCormick et al. [12] developed partitioned sampling, which, however, requires that the state space can be sliced. Sullivan et al. [13] proposed layered sampling using multi scale processing of images. In the field of Neural Network tracking [16] and filtering theory [19] outside computer vision, the Kalman particle and Unscented particle filters have been developed to improve the sampling mechanism. They adopt sub optimal proposal distributions, using the Kalman filter or unscented Kalman filter to integrate the most current ....
J.F.G. de Freitas, M. Niranjan, A. H. Gee, A. Doucet, "Sequential Monte Carlo Methods to Train Neural Networks Models," Neural Computation, vol. 12, no. 4, pp. 955-993, 2000.
Unknown - Self-citation (De freitas Doucet) (Correct)
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de Freitas, J. F. G., Niranjan, M., Gee, A. H. and Doucet, A. (2000). Sequential Monte Carlo methods to train neural network models, To appear in Neural Computation.
Unknown - Self-citation (De freitas Doucet) (Correct)
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de Freitas, J. F. G., Niranjan, M., Gee, A. H. and Doucet, A. (2000). Sequential Monte Carlo methods to train neural network models, Neural Computation 12(4): 955-993.
Rao-Blackwellised Particle Filtering for Fault Diagnosis - de Freitas (2001) (6 citations) Self-citation (De freitas) (Correct)
....filter [10] The importance sampling framework allows us to design more principled and clever proposal distributions. For instance, one can adopt suboptimal filters and other approximation methods that make use of the information available at time t to generate the proposal distribution [8] [6], 14] 16] In fact, in some restricted situations, one may interpret the likelihood as a distribution in terms of the states and sample from it directly. In doing so, the importance weights become equal to the transition prior [9] A selection scheme associates to each particle (bx 0:t ) ....
N de Freitas, M Niranjan, A H Gee, and A Doucet. Sequential Monte Carlo methods to train neural network models. Neural Computation, 12(4):955--993, 2000.
Bayesian Multioutput Feedforward Neural Network Comparison: A.. - Rossi, Vila (Correct)
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J. F. G. De Freitas, M. A. Niranjan, A. H. Gee, A. Doucet, "Sequential Monte Carlo methods to train neural network models", Neural Computation, vol 12, 4, pp. 955-993, 2000.
Bayesian Estimation by Sequential Monte Carlo Sampling for.. - Chen (2004) (Correct)
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J. F. G. de Freitas, M. Noranjan, A. H. Gee, and Arnaud Doucet. Sequential Monte Carlo methods to train neural network models. Neural Computation, 12:955--993, 2000. 114
The Unscented Kalman Filter - Wan, van der Merwe (2001) (12 citations) (Correct)
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J. F. G. de Freitas, M. Niranjan, A. H. Gee, and A. Doucet. Sequential Monte Carlo methods to train neural network models. Neural Computation, 12(4):955-993, 2000.
FastSLAM: A Factored Solution to the Simultaneous Localization.. - Montemerlo (2003) (44 citations) (Correct)
No context found.
N. de Freitas, M. Niranjan, A. Gee, and A. Doucet. Sequential monte carlo methods to train neural networks. Neural Computation, 12(4), 2000.
Numerical Integration by Cubature Formulae in Bayesian.. - van Wezel, Kosters (2003) (Correct)
No context found.
J. F. G. de Freitas, M. Niranjan, A. H. Gee, and A. Doucet, "Sequential Monte Carlo methods to train neural network models," Neural Computation, vol. 12, no. 4, pp. 955--993, 2000.
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